title
Decision Tree Algorithm & Analysis | Machine Learning Algorithm | Data Science Training | Edureka

description
( Data Science Training - https://www.edureka.co/data-science-r-programming-certification-course ) This Edureka Decision Tree tutorial will help you understand all the basics of Decision tree. This decision tree tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts, learn decision tree analysis along with examples. Below are the topics covered in this tutorial: 1) Machine Learning Introduction 2) Classification 3) Types of classifiers 4) Decision tree 5) How does Decision tree work? 6) Demo in R Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #decisiontree #Datasciencetutorial #Datasciencecourse #datascience How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at sales@edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "

detail
{'title': 'Decision Tree Algorithm & Analysis | Machine Learning Algorithm | Data Science Training | Edureka', 'heatmap': [{'end': 3329.147, 'start': 3178.38, 'weight': 0.865}, {'end': 4213.222, 'start': 4107.634, 'weight': 0.739}, {'end': 4402.871, 'start': 4346.311, 'weight': 0.873}], 'summary': 'Covers machine learning basics, decision tree algorithm, and its applications in predictive analytics, with real-world examples such as diabetes prediction, achieving approximately 71.13% accuracy in model evaluation and improvement.', 'chapters': [{'end': 122.728, 'segs': [{'end': 23.721, 'src': 'embed', 'start': 0.029, 'weight': 0, 'content': [{'end': 6.653, 'text': 'Welcome to this webinar on machine learning with decision trees organized by Edureka.', 'start': 0.029, 'duration': 6.624}, {'end': 16.519, 'text': 'And my name is Sriraj and I typically teach data science classes as well as big data Hadoop and Spark classes in Edureka.', 'start': 7.173, 'duration': 9.346}, {'end': 23.721, 'text': "But in my professional life, I'm also working as an architect in the space of retail domain.", 'start': 17.179, 'duration': 6.542}], 'summary': 'Webinar on machine learning with decision trees by sriraj, a data science instructor and retail domain architect.', 'duration': 23.692, 'max_score': 0.029, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k29.jpg'}, {'end': 97.527, 'src': 'embed', 'start': 51.313, 'weight': 1, 'content': [{'end': 57.137, 'text': 'Things like recommendations, demand forecasting, supply chain management, and those kind of things.', 'start': 51.313, 'duration': 5.824}, {'end': 64.803, 'text': 'So you can pretty much kind of see the applications of machine learning in a variety of domains and variety of applications.', 'start': 57.778, 'duration': 7.025}, {'end': 66.505, 'text': 'And retail is just one of them.', 'start': 65.284, 'duration': 1.221}, {'end': 68.886, 'text': "So that's kind of briefly about me.", 'start': 67.065, 'duration': 1.821}, {'end': 74.751, 'text': "So today's session, we are going to simply talk about the basics of machine learning.", 'start': 69.807, 'duration': 4.944}, {'end': 81.055, 'text': 'We start by understanding what exactly it means and what are the high-level types of machine learning.', 'start': 74.931, 'duration': 6.124}, {'end': 87.46, 'text': "And then we'll focus primarily on one of the techniques called predictive analytics.", 'start': 81.535, 'duration': 5.925}, {'end': 90.942, 'text': "That's the class of machine learning algorithms.", 'start': 87.86, 'duration': 3.082}, {'end': 97.527, 'text': 'And among those class of algorithms, we will look at decision trees as one particular example.', 'start': 91.462, 'duration': 6.065}], 'summary': "Machine learning has applications in various domains including retail, and today's session will focus on the basics and predictive analytics, specifically decision trees.", 'duration': 46.214, 'max_score': 51.313, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k51313.jpg'}], 'start': 0.029, 'title': 'Machine learning with decision trees', 'summary': 'Covers the basics of machine learning, emphasizing predictive analytics and decision trees. it is presented by sriraj, a data science and big data expert with over 10 years of experience in the retail domain and analytics.', 'chapters': [{'end': 122.728, 'start': 0.029, 'title': 'Machine learning with decision trees', 'summary': 'Discusses the basics of machine learning, focusing on predictive analytics and decision trees, presented by sriraj, a data science and big data expert with over 10 years of experience in the retail domain and analytics.', 'duration': 122.699, 'highlights': ["Sriraj has over 10, 12 years of experience in big data and analytics space, and holds a PhD in the area, specializing in analytics and data management. Sriraj's extensive experience and expertise in big data and analytics, with a PhD in the field, provides credibility to the webinar.", "Machine learning is applied to various business issues in retail, including recommendations, demand forecasting, and supply chain management. Machine learning's diverse applications in retail, such as demand forecasting and supply chain management, demonstrate its impact on business operations.", 'The session covers the basics of machine learning, high-level types of machine learning, and primarily focuses on predictive analytics and decision trees. The webinar delves into fundamental aspects of machine learning, with a specific focus on predictive analytics and decision trees, providing valuable insights to the audience.']}], 'duration': 122.699, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k29.jpg', 'highlights': ["Sriraj's extensive experience and expertise in big data and analytics, with a PhD in the field, provides credibility to the webinar.", "Machine learning's diverse applications in retail, such as demand forecasting and supply chain management, demonstrate its impact on business operations.", 'The webinar delves into fundamental aspects of machine learning, with a specific focus on predictive analytics and decision trees, providing valuable insights to the audience.']}, {'end': 837.867, 'segs': [{'end': 195.747, 'src': 'embed', 'start': 170.094, 'weight': 0, 'content': [{'end': 178.246, 'text': "And then I'll also show some brief demo of this particular algorithm in the context of our programming language.", 'start': 170.094, 'duration': 8.152}, {'end': 181.751, 'text': 'So, to begin with, what is machine learning?', 'start': 179.628, 'duration': 2.123}, {'end': 184.757, 'text': 'And if you look at Wikipedia,', 'start': 183.056, 'duration': 1.701}, {'end': 194.286, 'text': 'it says machine learning is a subfield of computer science that provides computers with the ability to learn without being explicitly programmed.', 'start': 184.757, 'duration': 9.529}, {'end': 195.747, 'text': 'And this was a definition.', 'start': 194.606, 'duration': 1.141}], 'summary': 'Machine learning is a subfield of computer science that enables computers to learn without explicit programming.', 'duration': 25.653, 'max_score': 170.094, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k170094.jpg'}, {'end': 257.344, 'src': 'embed', 'start': 222.668, 'weight': 1, 'content': [{'end': 227.611, 'text': "And that's kind of the considered the birth of artificial intelligence.", 'start': 222.668, 'duration': 4.943}, {'end': 235.815, 'text': "He imagined a computer or a software or a program that can mimic as if it's a human being.", 'start': 227.891, 'duration': 7.924}, {'end': 241.619, 'text': "And that's kind of the higher level idea behind artificial intelligence.", 'start': 236.516, 'duration': 5.103}, {'end': 244.02, 'text': 'Can we make machines smarter right?', 'start': 241.759, 'duration': 2.261}, {'end': 257.344, 'text': 'And artificial intelligence is a huge umbrella of variety of techniques and machine learning is kind of can be considered as a sub field within this umbrella of artificial intelligence.', 'start': 244.78, 'duration': 12.564}], 'summary': 'Birth of ai: machines mimicking humans, machine learning as subfield.', 'duration': 34.676, 'max_score': 222.668, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k222668.jpg'}, {'end': 411.936, 'src': 'embed', 'start': 380.67, 'weight': 2, 'content': [{'end': 385.871, 'text': 'Whereas the second approach, which is kind of considered as machine learning, operates like this.', 'start': 380.67, 'duration': 5.201}, {'end': 401.214, 'text': 'We provide the software a variety of examples of human face images, as well as non-human images, something like a mountain or a landscape, or a bird,', 'start': 386.752, 'duration': 14.462}, {'end': 402.514, 'text': 'or something like that, right?', 'start': 401.214, 'duration': 1.3}, {'end': 407.075, 'text': 'So we mix human faces and non-human faces.', 'start': 402.954, 'duration': 4.121}, {'end': 411.936, 'text': 'And we give all of these as examples to the software.', 'start': 408.534, 'duration': 3.402}], 'summary': 'Machine learning approach uses a variety of human and non-human images as examples for software training.', 'duration': 31.266, 'max_score': 380.67, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k380670.jpg'}, {'end': 550.414, 'src': 'embed', 'start': 520.754, 'weight': 3, 'content': [{'end': 528.639, 'text': "it is able to figure out the differences between something that we want and something that we don't want in a particular task, right?", 'start': 520.754, 'duration': 7.885}, {'end': 530.28, 'text': "So that's the machine learning.", 'start': 528.759, 'duration': 1.521}, {'end': 536.885, 'text': "That's exactly, I mean, that's human learning, and that's exactly how machine learning techniques also operate.", 'start': 530.601, 'duration': 6.284}, {'end': 541.568, 'text': 'You give lots of training data, which is examples, okay?', 'start': 537.525, 'duration': 4.043}, {'end': 550.414, 'text': 'Just like we are showing the kid lots of images about dogs and cats and human faces, we give lots of examples to the computer program.', 'start': 541.588, 'duration': 8.826}], 'summary': 'Machine learning involves providing lots of training data to help a computer program differentiate between desirable and undesirable outcomes.', 'duration': 29.66, 'max_score': 520.754, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k520754.jpg'}, {'end': 646.332, 'src': 'embed', 'start': 616.587, 'weight': 4, 'content': [{'end': 621.551, 'text': 'the brain improves by getting that feedback and then improving the mental model.', 'start': 616.587, 'duration': 4.964}, {'end': 624.594, 'text': "That's exactly the feedback loop that we are seeing here.", 'start': 621.972, 'duration': 2.622}, {'end': 634.102, 'text': 'So you can clearly see the analogies between the way our human brain works and the way machine learning models also work.', 'start': 624.954, 'duration': 9.148}, {'end': 635.343, 'text': "It's exactly the same.", 'start': 634.322, 'duration': 1.021}, {'end': 639.747, 'text': "It's kind of mimicking that process through computer programs.", 'start': 635.383, 'duration': 4.364}, {'end': 646.332, 'text': "I hope that it's clear about what a machine learning technique could entail.", 'start': 641.208, 'duration': 5.124}], 'summary': 'Feedback improves brain and machine learning models, mimicking human brain process.', 'duration': 29.745, 'max_score': 616.587, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k616587.jpg'}, {'end': 709.037, 'src': 'embed', 'start': 685.2, 'weight': 5, 'content': [{'end': 692.326, 'text': 'you can come up with so many examples that we see in our real world that are relating to machine learning.', 'start': 685.2, 'duration': 7.126}, {'end': 698.712, 'text': 'Actually, we are surrounded by machine learning applications in our everyday life.', 'start': 692.927, 'duration': 5.785}, {'end': 709.037, 'text': 'As soon as I go to Google.com, there are lots of advertisements, and those advertisements are tailor-made for my browsing behavior.', 'start': 699.426, 'duration': 9.611}], 'summary': 'Machine learning is prevalent in everyday life, seen in tailored advertisements on google.com.', 'duration': 23.837, 'max_score': 685.2, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k685200.jpg'}], 'start': 123.908, 'title': 'Machine learning concepts', 'summary': 'Introduces machine learning, its distinction from traditional programming, and its application in face recognition software. it also explores the analogies between human and machine learning, emphasizing learning through examples and feedback, and real-world applications of machine learning.', 'chapters': [{'end': 459.473, 'start': 123.908, 'title': 'Introduction to machine learning', 'summary': 'Introduces the concept of machine learning, discussing its definition and its relationship to artificial intelligence, while emphasizing the distinction between traditional programming and machine learning approaches, specifically in the context of developing a face recognition software.', 'duration': 335.565, 'highlights': ['The concept of machine learning is introduced, highlighting its subfield of computer science that enables computers to learn without explicit programming.', 'The chapter discusses the historical context of machine learning, referencing Alan Turing and the birth of artificial intelligence, emphasizing the aspiration to make machines smarter.', 'A comparison is drawn between traditional programming and machine learning approaches in developing a face recognition software, distinguishing between manual feature encoding and learning from labeled examples.']}, {'end': 837.867, 'start': 459.493, 'title': 'Analogies between human and machine learning', 'summary': 'Discusses the similarities between human learning and machine learning techniques, emphasizing the concept of learning through examples and feedback, and provides real-world examples of machine learning applications in everyday life.', 'duration': 378.374, 'highlights': ['Machine learning is akin to human learning, where exposure to examples enables the brain to distinguish between different classes of data points. The chapter draws parallels between human learning and machine learning, highlighting the role of exposure to examples in enabling the brain to distinguish between different classes of data points, such as images, audio samples, or credit card transactions.', 'Feedback loop and continuous learning are fundamental concepts in both human and machine learning, allowing for improvement of mental models or models built by computer programs. The concept of the feedback loop and continuous learning is emphasized in both human and machine learning, enabling improvement of mental models or computer-built models by learning from mistakes and receiving feedback.', 'Machine learning applications are prevalent in everyday life, including personalized advertisements, email filters, and product recommendations on e-commerce platforms. The chapter provides real-world examples of machine learning applications in everyday life, such as personalized advertisements based on browsing behavior, advanced email filters for spam detection, and product recommendations on e-commerce websites utilizing consumer demographics and purchase history.']}], 'duration': 713.959, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k123908.jpg', 'highlights': ['Machine learning enables computers to learn without explicit programming.', 'Alan Turing and the birth of artificial intelligence are referenced, emphasizing the aspiration to make machines smarter.', 'A comparison is drawn between traditional programming and machine learning approaches in developing face recognition software.', 'Machine learning is akin to human learning, where exposure to examples enables the brain to distinguish between different classes of data points.', 'Feedback loop and continuous learning are fundamental concepts in both human and machine learning.', 'Machine learning applications are prevalent in everyday life, including personalized advertisements, email filters, and product recommendations on e-commerce platforms.']}, {'end': 1095.586, 'segs': [{'end': 923.588, 'src': 'embed', 'start': 838.447, 'weight': 0, 'content': [{'end': 842.129, 'text': 'And Amazon has pioneered in this recommendation systems.', 'start': 838.447, 'duration': 3.682}, {'end': 849.212, 'text': 'And nowadays recommendation system is like the most common applications that you can think of in every single business.', 'start': 842.729, 'duration': 6.483}, {'end': 856.356, 'text': 'Whether you are an insurance company, whether you are a bank or whether you are a medical product provider.', 'start': 849.773, 'duration': 6.583}, {'end': 861.378, 'text': 'everywhere you can think of cases of recommendation engine right?', 'start': 856.916, 'duration': 4.462}, {'end': 864.76, 'text': "And that's also machine learning example.", 'start': 861.939, 'duration': 2.821}, {'end': 871.744, 'text': 'So you can kind of look at many, many examples of machine learning surrounding us in our everyday life.', 'start': 864.82, 'duration': 6.924}, {'end': 883.861, 'text': 'and there are many types of machine learning tasks, and one most popular one is the predictive analytics, also known as classification.', 'start': 873.072, 'duration': 10.789}, {'end': 892.487, 'text': 'okay, and in fact, like the examples that we talked about, or couple of them are about classification like face recognition.', 'start': 883.861, 'duration': 8.626}, {'end': 894.729, 'text': 'to begin with, what is that program doing?', 'start': 892.487, 'duration': 2.242}, {'end': 902.403, 'text': 'It is able to classify a given image to be a human face or non-human face.', 'start': 895.362, 'duration': 7.041}, {'end': 905.004, 'text': "So that's a classification.", 'start': 902.884, 'duration': 2.12}, {'end': 906.924, 'text': 'Similarly, email filter.', 'start': 905.164, 'duration': 1.76}, {'end': 912.866, 'text': 'It is able to classify a given email to be spam or non-spam.', 'start': 907.425, 'duration': 5.441}, {'end': 915.166, 'text': "That's again, classifier.", 'start': 913.466, 'duration': 1.7}, {'end': 920.967, 'text': "So you're taking the data and then dividing it into different classes, different categories.", 'start': 915.546, 'duration': 5.421}, {'end': 923.588, 'text': "And that's the classification.", 'start': 922.408, 'duration': 1.18}], 'summary': 'Amazon pioneered recommendation systems, prevalent in various industries, utilizing machine learning for tasks like predictive analytics and classification.', 'duration': 85.141, 'max_score': 838.447, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k838447.jpg'}, {'end': 1048.655, 'src': 'embed', 'start': 999.794, 'weight': 3, 'content': [{'end': 1010.859, 'text': 'So you need to show millions and millions of examples and then The more data you feed, typically the better it becomes, right? Just like our brain.', 'start': 999.794, 'duration': 11.065}, {'end': 1019.745, 'text': "When I'm learning a new concept, let's say in mathematics, differential equations, the more examples I practice, the better I become right?", 'start': 1011.479, 'duration': 8.266}, {'end': 1021.666, 'text': "But after a point there's a saturation.", 'start': 1020.105, 'duration': 1.561}, {'end': 1026.229, 'text': "After a point, I'm pretty much no differential equations in and out.", 'start': 1022.106, 'duration': 4.123}, {'end': 1028.19, 'text': 'Exactly like spam filter.', 'start': 1026.888, 'duration': 1.302}, {'end': 1029.59, 'text': 'After a point.', 'start': 1028.25, 'duration': 1.34}, {'end': 1031.712, 'text': 'the learning kind of flattens right?', 'start': 1029.59, 'duration': 2.122}, {'end': 1035.448, 'text': 'Can you please give some other examples on predictive analytics?', 'start': 1032.486, 'duration': 2.962}, {'end': 1039.069, 'text': 'maybe an industry example where a decision is taken? Yeah.', 'start': 1035.448, 'duration': 3.621}, {'end': 1044.873, 'text': 'Actually, I mean, spam filter is a real-world example, right? Google does it, or Yahoo does it.', 'start': 1039.55, 'duration': 5.323}, {'end': 1048.655, 'text': "And let's say credit card transactions.", 'start': 1045.853, 'duration': 2.802}], 'summary': "More data improves learning, but there's a saturation point; examples include spam filter and credit card transactions.", 'duration': 48.861, 'max_score': 999.794, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k999794.jpg'}], 'start': 838.447, 'title': 'Machine learning in everyday life', 'summary': 'Discusses the prevalent applications of recommendation systems in various industries, emphasizing machine learning in predictive analytics and classification tasks, with real-world examples such as face recognition and email filtering.', 'chapters': [{'end': 1095.586, 'start': 838.447, 'title': 'Machine learning in everyday life', 'summary': 'Discusses the widespread applications of recommendation systems in various industries, emphasizing the prevalence of machine learning, particularly in predictive analytics or classification tasks, and provides real-world examples such as face recognition and email filtering.', 'duration': 257.139, 'highlights': ['Recommendation systems have become common in various industries such as insurance, banking, and healthcare, with Amazon being a pioneer in this field. Recommendation systems have become common in various industries such as insurance, banking, and healthcare, with Amazon being a pioneer in this field.', 'Predictive analytics, also known as classification, is a popular type of machine learning task with applications like face recognition and email filtering. Predictive analytics, also known as classification, is a popular type of machine learning task with applications like face recognition and email filtering.', 'Classification involves categorizing new observations into different classes or categories, such as identifying human faces in images or classifying emails as spam or non-spam. Classification involves categorizing new observations into different classes or categories, such as identifying human faces in images or classifying emails as spam or non-spam.', 'The effectiveness of predictive analytics improves with more example data, similar to how the human brain learns through practice, but there is a saturation point where further learning plateaus. The effectiveness of predictive analytics improves with more example data, similar to how the human brain learns through practice, but there is a saturation point where further learning plateaus.', 'Real-world examples of predictive analytics include spam filtering by companies like Google and Yahoo, as well as decision-making in credit card transactions to identify potential fraud. Real-world examples of predictive analytics include spam filtering by companies like Google and Yahoo, as well as decision-making in credit card transactions to identify potential fraud.']}], 'duration': 257.139, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k838447.jpg', 'highlights': ['Recommendation systems are prevalent in industries like insurance, banking, and healthcare, with Amazon as a pioneer.', 'Predictive analytics, or classification, is widely used in machine learning for tasks like face recognition and email filtering.', 'Classification involves categorizing new observations into different classes or categories, such as identifying human faces in images or classifying emails as spam or non-spam.', 'The effectiveness of predictive analytics improves with more example data, similar to how the human brain learns through practice.', 'Real-world examples of predictive analytics include spam filtering by companies like Google and Yahoo, and decision-making in credit card transactions to identify potential fraud.']}, {'end': 1673.581, 'segs': [{'end': 1152.356, 'src': 'embed', 'start': 1118.788, 'weight': 0, 'content': [{'end': 1126.916, 'text': 'you can feed it to these machine learning models and then predict the risk of having certain disease right?', 'start': 1118.788, 'duration': 8.128}, {'end': 1136.328, 'text': "And in the later part of the class, when we see the demo, we'll see the data set where you're predicting the risk of having diabetes right?", 'start': 1127.237, 'duration': 9.091}, {'end': 1140.549, 'text': "So that's essentially a prediction problem again classification problem.", 'start': 1136.866, 'duration': 3.683}, {'end': 1152.356, 'text': "And if you go for, let's say, MRI scan or a bunch of other tests, there's always certain probability associated with the outcome of that result.", 'start': 1141.029, 'duration': 11.327}], 'summary': 'Machine learning models predict disease risk, e.g. diabetes, based on data and tests.', 'duration': 33.568, 'max_score': 1118.788, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k1118788.jpg'}, {'end': 1245.911, 'src': 'embed', 'start': 1220.003, 'weight': 1, 'content': [{'end': 1227.524, 'text': 'Every day they want to figure out which customers are likely to go out of my network and then join another network.', 'start': 1220.003, 'duration': 7.521}, {'end': 1228.804, 'text': "That's called churn.", 'start': 1227.984, 'duration': 0.82}, {'end': 1234.425, 'text': "That's again prediction because I'm trying to predict who is going to leave my network.", 'start': 1230.185, 'duration': 4.24}, {'end': 1238.586, 'text': 'Does that make sense? Okay, very good.', 'start': 1236.386, 'duration': 2.2}, {'end': 1245.911, 'text': 'And so those are the different applications where this predictive analytics can play a role right?', 'start': 1239.102, 'duration': 6.809}], 'summary': 'Predictive analytics used to identify customer churn and other applications.', 'duration': 25.908, 'max_score': 1220.003, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k1220003.jpg'}, {'end': 1367.297, 'src': 'embed', 'start': 1341.651, 'weight': 2, 'content': [{'end': 1346.396, 'text': 'So all of these are some form of predictive analytic techniques.', 'start': 1341.651, 'duration': 4.745}, {'end': 1357.649, 'text': 'Okay and to make the problem even more interesting or frustrating for a given dataset, for a given application,', 'start': 1347.321, 'duration': 10.328}, {'end': 1360.051, 'text': 'one algorithm may be better than the other algorithm.', 'start': 1357.649, 'duration': 2.402}, {'end': 1367.297, 'text': 'We can all say that decision tree is always the best algorithm compared to Naive Bayes, right?', 'start': 1360.732, 'duration': 6.565}], 'summary': 'Predictive analytics techniques vary in performance; decision tree may outperform naive bayes.', 'duration': 25.646, 'max_score': 1341.651, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k1341651.jpg'}, {'end': 1544.156, 'src': 'embed', 'start': 1519.921, 'weight': 3, 'content': [{'end': 1528.949, 'text': 'So what is a decision tree? Decision tree is basically a technique or a data structure that you build that help you in making your decisions.', 'start': 1519.921, 'duration': 9.028}, {'end': 1532.171, 'text': "And it's very, very common.", 'start': 1530.51, 'duration': 1.661}, {'end': 1536.433, 'text': "Even though we don't call it decision tree, we all use it in real world.", 'start': 1532.471, 'duration': 3.962}, {'end': 1544.156, 'text': "Let's say I'm a manager or a architect in computer science department in a company.", 'start': 1537.533, 'duration': 6.623}], 'summary': 'A decision tree is a widely used technique for decision-making in various fields, including computer science.', 'duration': 24.235, 'max_score': 1519.921, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k1519921.jpg'}], 'start': 1096.087, 'title': 'Predictive analytics applications', 'summary': 'Explores the applications of predictive analytics in healthcare and telecommunication, emphasizing disease risk prediction, customer churn, and various techniques including decision trees, random forest, and naive bayes, along with factors influencing algorithm selection and decision-making scenarios.', 'chapters': [{'end': 1294.001, 'start': 1096.087, 'title': 'Predictive analytics in healthcare and telecommunication', 'summary': 'Discusses the applications of predictive analytics in healthcare and telecommunication, highlighting its role in predicting disease risk and customer churn, as well as the challenges of accuracy and learning methods.', 'duration': 197.914, 'highlights': ['Predictive analytics is used in healthcare to predict the risk of certain diseases, such as diabetes, using machine learning models (e.g., demo dataset on diabetes risk prediction).', 'Accuracy of predictive analytics in healthcare, e.g., MRI and CT scans for heart-related diseases, is around 75%, leading to potential misdiagnosis and the need for further tests.', 'Telecommunication companies like Airtel use predictive analytics to identify customers likely to leave their network, known as churn, demonstrating the practical application of predictive analytics in customer behavior prediction.', 'The chapter emphasizes the importance of learning methods, drawing parallels between learning predictive analytics and learning basic tasks such as division, highlighting diverse teaching approaches and the ultimate goal of building a mental model for the task.']}, {'end': 1673.581, 'start': 1295.441, 'title': 'Predictive models and decision trees', 'summary': 'Discusses various predictive analytic techniques including decision trees, random forest, naive bayes, and the process of choosing an algorithm based on trial and testing, data quality, and decision factors. it also explains the concept of decision trees as a technique for making decisions in various scenarios.', 'duration': 378.14, 'highlights': ['The chapter discusses various predictive analytic techniques including decision trees, random forest, Naive Bayes, and the process of choosing an algorithm based on trial and testing, data quality, and decision factors. The chapter highlights the discussion on different predictive analytic techniques and the process of algorithm selection based on trial and testing, data quality, and decision factors.', 'The concept of decision trees is explained as a technique for making decisions in various scenarios, such as in a managerial or educational context. The explanation of decision trees as a technique for making decisions in managerial or educational scenarios is emphasized.']}], 'duration': 577.494, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k1096087.jpg', 'highlights': ['Predictive analytics is used in healthcare to predict the risk of certain diseases, such as diabetes, using machine learning models (e.g., demo dataset on diabetes risk prediction).', 'Telecommunication companies like Airtel use predictive analytics to identify customers likely to leave their network, known as churn, demonstrating the practical application of predictive analytics in customer behavior prediction.', 'The chapter discusses various predictive analytic techniques including decision trees, random forest, Naive Bayes, and the process of choosing an algorithm based on trial and testing, data quality, and decision factors.', 'The concept of decision trees is explained as a technique for making decisions in various scenarios, such as in a managerial or educational context.']}, {'end': 2625.835, 'segs': [{'end': 1836.584, 'src': 'embed', 'start': 1806.239, 'weight': 0, 'content': [{'end': 1808.859, 'text': "So that's the kind of decision tree that we are talking about.", 'start': 1806.239, 'duration': 2.62}, {'end': 1813.531, 'text': 'Okay, and similarly you can talk about many different examples.', 'start': 1809.909, 'duration': 3.622}, {'end': 1819.234, 'text': 'Like this is another example which is very real world application as one of you were asking.', 'start': 1813.631, 'duration': 5.603}, {'end': 1822.316, 'text': "It's a credit risk detection right?", 'start': 1819.895, 'duration': 2.421}, {'end': 1833.122, 'text': 'When somebody is giving you a financial institution, is giving you a loan or a credit or something, they analyze the risk right?', 'start': 1822.796, 'duration': 10.326}, {'end': 1836.584, 'text': 'Whether you can pay back that particular loan.', 'start': 1833.442, 'duration': 3.142}], 'summary': 'Decision trees are used in real-world applications like credit risk detection to analyze the risk of loan repayment.', 'duration': 30.345, 'max_score': 1806.239, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k1806239.jpg'}, {'end': 2073.139, 'src': 'embed', 'start': 2045.359, 'weight': 3, 'content': [{'end': 2051.603, 'text': 'So I have weather information of last 14 days and I also have the decisions that we have made.', 'start': 2045.359, 'duration': 6.244}, {'end': 2054.906, 'text': 'Whether we played or not played.', 'start': 2052.764, 'duration': 2.142}, {'end': 2058.228, 'text': 'Not just the decisions, whether we actually played or not.', 'start': 2055.746, 'duration': 2.482}, {'end': 2059.768, 'text': "That's the actual data.", 'start': 2058.507, 'duration': 1.261}, {'end': 2065.453, 'text': 'And then we can predict for future based on those 14 days of data set.', 'start': 2060.79, 'duration': 4.663}, {'end': 2067.875, 'text': 'So this becomes our training data.', 'start': 2066.254, 'duration': 1.621}, {'end': 2073.139, 'text': "Now using the training data, let's see how to build a decision tree.", 'start': 2068.476, 'duration': 4.663}], 'summary': 'Analyzed 14 days of weather data to make decisions and build a decision tree for future predictions.', 'duration': 27.78, 'max_score': 2045.359, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k2045359.jpg'}, {'end': 2431.123, 'src': 'embed', 'start': 2402.767, 'weight': 2, 'content': [{'end': 2406.268, 'text': "So that's my pretty much decision tree that I built right?", 'start': 2402.767, 'duration': 3.501}, {'end': 2412.451, 'text': "Now you tell me any day's forecast outlook, humidity and wind combination.", 'start': 2406.628, 'duration': 5.823}, {'end': 2416.152, 'text': 'I can tell you whether to play outside or not, right?', 'start': 2412.451, 'duration': 3.701}, {'end': 2417.233, 'text': 'Can you do that?', 'start': 2416.713, 'duration': 0.52}, {'end': 2418.693, 'text': 'We all can do that.', 'start': 2417.952, 'duration': 0.741}, {'end': 2428.781, 'text': 'We simply need to go over different branches and then finally reach a leaf node, the last node, where the actual decision is made.', 'start': 2418.753, 'duration': 10.028}, {'end': 2431.123, 'text': 'This is not play, this is play.', 'start': 2429.041, 'duration': 2.082}], 'summary': 'Built a decision tree to determine play based on weather conditions.', 'duration': 28.356, 'max_score': 2402.767, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k2402767.jpg'}], 'start': 1674.853, 'title': 'Decision tree algorithm for predictive analysis', 'summary': 'Delves into the decision tree algorithm, illustrating its application in email classification, credit risk analysis, and predicting outdoor play based on weather features. it emphasizes data-driven decision-making and feature selection.', 'chapters': [{'end': 1934.154, 'start': 1674.853, 'title': 'Decision tree algorithm', 'summary': 'Discusses the decision tree algorithm, a series of alternatives exploring to reach a particular decision point, with examples such as email classification and credit risk analysis.', 'duration': 259.301, 'highlights': ['The decision tree algorithm is a series of alternatives exploring to reach a particular decision point. It involves making decisions based on different paths and outcomes, illustrated through examples such as email classification and credit risk analysis.', 'Examples of decision tree applications include email classification and credit risk analysis. The algorithm is used to predict outcomes, such as classifying emails as spam or not spam, and analyzing the risk of loan applicants based on factors like income and credit history.', 'The decision tree algorithm can be used for real-world applications like credit risk analysis when assessing loan applicants. It involves analyzing factors such as income and credit history to determine the risk level of an applicant, impacting their eligibility for a loan.']}, {'end': 2625.835, 'start': 1935.85, 'title': 'Decision tree for predicting outdoor play', 'summary': 'Discusses using decision tree to predict whether to play outside based on weather features like outlook, humidity, and wind speed, with emphasis on data-driven decision-making and feature selection.', 'duration': 689.985, 'highlights': ['Decision tree uses outlook, humidity, and wind features to predict outdoor play decisions based on historical training data. The chapter focuses on using a decision tree to predict whether to play outside based on weather features like outlook, humidity, and wind speed, emphasizing the use of historical training data.', 'The decision tree is constructed based on historical data of weather conditions and play decisions, with a focus on feature values and their distribution. The decision tree is constructed using historical data of weather conditions and play decisions, with a focus on analyzing feature values and their distribution to make predictive decisions.', 'The decision tree splits the data based on weather outlook, humidity, and wind values, leading to data-driven play or not play decisions. The decision tree uses weather outlook, humidity, and wind values to split the data and make data-driven decisions on whether to play outside or not.', 'The chapter emphasizes the importance of data-driven decision-making and feature selection in constructing a decision tree for predicting outdoor play. The chapter emphasizes the significance of using historical data to make data-driven decisions and select relevant features in constructing a decision tree for predicting outdoor play.']}], 'duration': 950.982, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k1674853.jpg', 'highlights': ['The decision tree algorithm involves making decisions based on different paths and outcomes, illustrated through examples such as email classification and credit risk analysis.', 'The decision tree algorithm can be used for real-world applications like credit risk analysis when assessing loan applicants, involving analyzing factors such as income and credit history to determine the risk level of an applicant.', 'Decision tree uses outlook, humidity, and wind features to predict outdoor play decisions based on historical training data, emphasizing the use of historical training data.', 'The decision tree is constructed based on historical data of weather conditions and play decisions, with a focus on analyzing feature values and their distribution to make predictive decisions.', 'The decision tree splits the data based on weather outlook, humidity, and wind values, leading to data-driven play or not play decisions.']}, {'end': 3724.544, 'segs': [{'end': 3153.374, 'src': 'embed', 'start': 3127.887, 'weight': 1, 'content': [{'end': 3133.814, 'text': 'Does it make sense, everyone? So to begin with, in my original data, there is some measure of entropy.', 'start': 3127.887, 'duration': 5.927}, {'end': 3136.977, 'text': 'We can calculate, okay, ignoring the equations.', 'start': 3134.214, 'duration': 2.763}, {'end': 3139.34, 'text': 'I just want you to capture the idea.', 'start': 3137.158, 'duration': 2.182}, {'end': 3143.044, 'text': 'We have some level of entropy, some level of uncertainty,', 'start': 3139.861, 'duration': 3.183}, {'end': 3151.132, 'text': 'but we are dividing the data such that each resulting partition has less amount of uncertainty.', 'start': 3143.044, 'duration': 8.088}, {'end': 3153.374, 'text': 'okay?. Which means easy to predict.', 'start': 3151.132, 'duration': 2.242}], 'summary': 'Original data has entropy; partitioning reduces uncertainty for easier prediction.', 'duration': 25.487, 'max_score': 3127.887, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k3127887.jpg'}, {'end': 3329.147, 'src': 'heatmap', 'start': 3178.38, 'weight': 0.865, 'content': [{'end': 3181.943, 'text': 'Ultimately, you need to compute that value, right? And that depends on the probabilities.', 'start': 3178.38, 'duration': 3.563}, {'end': 3186.828, 'text': 'Without going into the details, you can just plug in these probability values.', 'start': 3182.384, 'duration': 4.444}, {'end': 3190.952, 'text': "Like, if I say nine s's and five no's, what's the probability of yes?", 'start': 3187.288, 'duration': 3.664}, {'end': 3193.694, 'text': 'Nine divided by nine plus five, right?', 'start': 3191.432, 'duration': 2.262}, {'end': 3204.284, 'text': "So if I have in my data nine s's and five no's, then probability of s would be nine divided by 14..", 'start': 3194.175, 'duration': 10.109}, {'end': 3206.964, 'text': 'Probability of no is five divided by 14.', 'start': 3204.284, 'duration': 2.68}, {'end': 3209.685, 'text': 'Those are the two probabilities for my outcome.', 'start': 3206.964, 'duration': 2.721}, {'end': 3214.346, 'text': 'Then I simply plug it into this equation and then I get a value.', 'start': 3210.245, 'duration': 4.101}, {'end': 3217.626, 'text': "That's the entropy in the given data set.", 'start': 3214.766, 'duration': 2.86}, {'end': 3224.388, 'text': 'And then we can do some more math and say, oh, if you divide based on certain column.', 'start': 3218.286, 'duration': 6.102}, {'end': 3228.448, 'text': 'In this example, some contact column.', 'start': 3225.568, 'duration': 2.88}, {'end': 3232.489, 'text': 'What are the entropies of my resulting partitions?', 'start': 3229.948, 'duration': 2.541}, {'end': 3243.072, 'text': "There's some math I don't wanna go into confuse you more, but there is some math to figure out what's the entropy of the resulting partitions.", 'start': 3233.426, 'duration': 9.646}, {'end': 3245.874, 'text': 'And then you simply take the subtraction.', 'start': 3243.432, 'duration': 2.442}, {'end': 3252.358, 'text': "What's the entropy before and what's the entropy after partitioning by a particular attribute.", 'start': 3246.314, 'duration': 6.044}, {'end': 3261.183, 'text': "And what's our goal? We want to take a difficult to classify problem and then turn it into easy to classify problem.", 'start': 3253.158, 'duration': 8.025}, {'end': 3264.695, 'text': 'So we want to reduce the entropy as much as possible.', 'start': 3261.533, 'duration': 3.162}, {'end': 3271.078, 'text': 'So we want this difference to be as high as possible, before minus after entropy.', 'start': 3265.235, 'duration': 5.843}, {'end': 3280.523, 'text': 'If the difference is very high, that means we are dividing the problems into very small entropy partitions.', 'start': 3272.519, 'duration': 8.004}, {'end': 3290.508, 'text': 'So you simply compute this difference, which is called as an information gain, that metric for all the attributes in your data, like this.', 'start': 3282.004, 'duration': 8.504}, {'end': 3294.111, 'text': 'If I divide it on P outcome, this is my information gain.', 'start': 3291.049, 'duration': 3.062}, {'end': 3298.354, 'text': 'If I divide based on my contact column, this is my information gain.', 'start': 3294.712, 'duration': 3.642}, {'end': 3300.396, 'text': 'So you do that for all the columns.', 'start': 3298.795, 'duration': 1.601}, {'end': 3304.559, 'text': 'And then you simply pick the one with maximum value.', 'start': 3300.996, 'duration': 3.563}, {'end': 3306.14, 'text': 'In this case, P outcome.', 'start': 3304.939, 'duration': 1.201}, {'end': 3312.725, 'text': 'So what that means is, given the data set, the first thing I should check is P outcome.', 'start': 3306.6, 'duration': 6.125}, {'end': 3314.687, 'text': 'And then based on that, you divide.', 'start': 3313.266, 'duration': 1.421}, {'end': 3316.049, 'text': 'Okay, like this.', 'start': 3315.347, 'duration': 0.702}, {'end': 3319.376, 'text': 'P outcome, whatever is the set of values.', 'start': 3316.69, 'duration': 2.686}, {'end': 3323.366, 'text': 'And then you make, again, same calculation.', 'start': 3320.305, 'duration': 3.061}, {'end': 3329.147, 'text': 'Out of all the columns, compute the information gain for this particular subset of data.', 'start': 3323.806, 'duration': 5.341}], 'summary': 'Using probabilities and information gain, we aim to reduce entropy for easy classification.', 'duration': 150.767, 'max_score': 3178.38, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k3178380.jpg'}, {'end': 3576.184, 'src': 'embed', 'start': 3544.093, 'weight': 3, 'content': [{'end': 3546.494, 'text': 'What does that toolbox contain? Different techniques.', 'start': 3544.093, 'duration': 2.401}, {'end': 3556.399, 'text': "And for a given data, for a given problem, like if I'm predicting diabetes or predicting credit risk or predicting something else, email spam.", 'start': 3547.275, 'duration': 9.124}, {'end': 3564.243, 'text': 'For each of these different problem and for a given data, couple of these techniques may perform better than the other techniques.', 'start': 3557.12, 'duration': 7.123}, {'end': 3567.885, 'text': "Okay? Make sense? That's a great question.", 'start': 3565.004, 'duration': 2.881}, {'end': 3576.184, 'text': "On what basis we can select an algorithm? What do you think? It's based on the performance of an algorithm, something like accuracy.", 'start': 3568.025, 'duration': 8.159}], 'summary': 'Various techniques perform differently for different problems, based on algorithm performance like accuracy.', 'duration': 32.091, 'max_score': 3544.093, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k3544093.jpg'}, {'end': 3621.457, 'src': 'embed', 'start': 3593.114, 'weight': 2, 'content': [{'end': 3598.097, 'text': 'So accuracy is one measure based on which I can select an algorithm,', 'start': 3593.114, 'duration': 4.983}, {'end': 3604.751, 'text': 'whether decision tree is doing good for my data or logistic regression is doing good for my data, right?', 'start': 3598.097, 'duration': 6.654}, {'end': 3607.132, 'text': 'Again, accuracy is only one measure.', 'start': 3605.111, 'duration': 2.021}, {'end': 3612.194, 'text': 'There are a couple of other important measures as well, like precision and recall.', 'start': 3607.612, 'duration': 4.582}, {'end': 3617.775, 'text': 'There are slight differences between these different measures, but you have a bunch of different measures.', 'start': 3612.214, 'duration': 5.561}, {'end': 3621.457, 'text': 'And based on those measures, select your algorithm.', 'start': 3618.496, 'duration': 2.961}], 'summary': 'Accuracy, precision, and recall are important measures for algorithm selection.', 'duration': 28.343, 'max_score': 3593.114, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k3593114.jpg'}, {'end': 3664.355, 'src': 'embed', 'start': 3635.415, 'weight': 4, 'content': [{'end': 3638.716, 'text': 'And it not just teach you technique, but also different concepts.', 'start': 3635.415, 'duration': 3.301}, {'end': 3641.998, 'text': 'Like decision tree is teaching you the concept of entropy.', 'start': 3638.997, 'duration': 3.001}, {'end': 3647.482, 'text': 'So, those fundamental concepts, these techniques will teach you.', 'start': 3643.099, 'duration': 4.383}, {'end': 3653.267, 'text': 'Something like k-means clustering, decision trees, linear regression, Naive Bayes.', 'start': 3647.962, 'duration': 5.305}, {'end': 3658.411, 'text': 'So, there are a bunch of fundamental techniques that any data scientist must know.', 'start': 3653.687, 'duration': 4.724}, {'end': 3664.355, 'text': "And you can easily get this list if you open up any university's data science class.", 'start': 3658.911, 'duration': 5.444}], 'summary': 'Fundamental techniques like k-means clustering, decision trees, linear regression, naive bayes are essential for data scientists.', 'duration': 28.94, 'max_score': 3635.415, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k3635415.jpg'}, {'end': 3704.721, 'src': 'embed', 'start': 3680.58, 'weight': 0, 'content': [{'end': 3687.626, 'text': "And even in decision trees, you can go into the math and then there's a lot of complexities on decision trees itself.", 'start': 3680.58, 'duration': 7.046}, {'end': 3697.495, 'text': 'Entropy is one intuitive way of figuring it out, but there are 20 different ways people have found out how to choose that attribute.', 'start': 3688.747, 'duration': 8.748}, {'end': 3703.6, 'text': 'Instead of entropy, somebody says, look at something called as minimum description length.', 'start': 3698.015, 'duration': 5.585}, {'end': 3704.721, 'text': "That's another metric.", 'start': 3703.84, 'duration': 0.881}], 'summary': 'Decision trees have 20 different ways to choose attributes, including entropy and minimum description length.', 'duration': 24.141, 'max_score': 3680.58, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k3680580.jpg'}], 'start': 2626.636, 'title': 'Understanding entropy and data science algorithms', 'summary': 'Delves into the use of entropy in decision making and its impact on predictability, with examples from us and indian elections. it also explains the selection of data science algorithms based on performance measures such as accuracy, precision, and recall.', 'chapters': [{'end': 2680.19, 'start': 2626.636, 'title': 'Entropy in information theory', 'summary': 'Explains how entropy, a mathematical concept from information theory, is used to identify the most informative attribute in the context of machine learning, and its historical association with communication methods.', 'duration': 53.554, 'highlights': ['Entropy, a mathematical concept from information theory, is used to identify the most informative attribute in machine learning.', 'Entropy was first used in the context of information theory for communicating information before telephone networks came into existence.', 'The concept of entropy is based on the idea of passing information from one place to another in a concise manner.']}, {'end': 3462.743, 'start': 2680.21, 'title': 'Understanding entropy and decision trees', 'summary': 'Explains the concept of entropy as a measure of uncertainty in data, illustrating its impact on predictability using examples from us and indian elections. it then delves into the role of entropy in decision making, how it relates to data partitioning, and its significance in decision tree algorithms.', 'duration': 782.533, 'highlights': ['Entropy measures the amount of uncertainty or randomness in data and is illustrated using examples from US and Indian elections. Entropy is a measure of uncertainty or randomness in data, as demonstrated through examples from US and Indian elections.', 'The relationship between entropy and predictability is explained, showing how low entropy makes predictions easier, while high entropy makes predictions difficult. The connection between entropy and predictability is detailed, highlighting that low entropy makes predictions easier, while high entropy makes predictions difficult.', 'The concept of entropy in decision making and its role in data partitioning is discussed, emphasizing the goal of reducing entropy to facilitate easier prediction. The role of entropy in decision making and data partitioning is explained, with a focus on reducing entropy to simplify prediction.', 'The process of using entropy in decision tree algorithms, including the calculation of information gain and attribute selection, is outlined. The utilization of entropy in decision tree algorithms, involving the calculation of information gain and attribute selection, is outlined.', 'The advantages and limitations of decision trees, such as overfitting and performance, are briefly mentioned, emphasizing their common usage and practical performance. The chapter briefly touches on the pros and cons of decision trees, highlighting their common usage and practical performance despite potential limitations.']}, {'end': 3724.544, 'start': 3462.743, 'title': 'Selecting data science algorithms', 'summary': 'Explains that different data science algorithms are like tools in a toolbox, and the selection of algorithms is based on various performance measures such as accuracy, precision, and recall, with fundamental techniques being necessary for any data scientist to know.', 'duration': 261.801, 'highlights': ['The selection of algorithms is based on various performance measures such as accuracy, precision, and recall. The chapter explains that the basis for selecting an algorithm is the performance of the algorithm, with measures like accuracy, precision, and recall being important factors.', 'Different data science algorithms are like tools in a toolbox, and the selection of algorithms depends on the specific problem and data. The chapter uses the analogy of different tools in a toolbox to illustrate that for a given problem and data, different algorithms would be more appropriate and perform better than others.', 'Fundamental techniques such as decision trees, linear regression, and Naive Bayes are necessary for any data scientist to know. The chapter emphasizes that fundamental techniques like decision trees, linear regression, and Naive Bayes are essential for any data scientist to understand, as they teach fundamental concepts and form the basis for more complex variations of these techniques.']}], 'duration': 1097.908, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k2626636.jpg', 'highlights': ['Entropy measures uncertainty in data, aiding attribute selection in machine learning.', "Entropy's role in decision making and data partitioning is explained, emphasizing its impact on predictability.", 'The selection of algorithms is based on performance measures like accuracy, precision, and recall.', 'Different data science algorithms are compared to tools in a toolbox, each suited for specific problems.', 'Fundamental techniques like decision trees, linear regression, and Naive Bayes are essential for data scientists.']}, {'end': 4286.567, 'segs': [{'end': 3807.464, 'src': 'embed', 'start': 3782.011, 'weight': 0, 'content': [{'end': 3789.796, 'text': "And what does this data set represent? It's a data set about women patients who has participated in some diabetic study.", 'start': 3782.011, 'duration': 7.785}, {'end': 3794.775, 'text': 'And for these patients, they collected different features.', 'start': 3791.172, 'duration': 3.603}, {'end': 3807.464, 'text': "Things like number of times a particular woman got pregnant and what's the glucose concentration in her blood, diastolic blood pressure,", 'start': 3796.076, 'duration': 11.388}], 'summary': 'Data set represents women in diabetic study with various collected features.', 'duration': 25.453, 'max_score': 3782.011, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k3782011.jpg'}, {'end': 3887.297, 'src': 'embed', 'start': 3860.437, 'weight': 1, 'content': [{'end': 3864.481, 'text': "And we're gonna predict that based on all these different columns.", 'start': 3860.437, 'duration': 4.044}, {'end': 3865.843, 'text': "That's the problem definition.", 'start': 3864.601, 'duration': 1.242}, {'end': 3872.889, 'text': "So that's my data and that's our goal to build a decision tree, to be even specific,", 'start': 3866.846, 'duration': 6.043}, {'end': 3878.552, 'text': 'that can predict whether the person is gonna have risk of diabetes or not.', 'start': 3872.889, 'duration': 5.663}, {'end': 3887.297, 'text': 'Now for that, of course we can run our algorithm that we just learned using the entropy and then build a model, great.', 'start': 3879.232, 'duration': 8.065}], 'summary': 'Build a decision tree to predict diabetes risk using learned algorithm and data.', 'duration': 26.86, 'max_score': 3860.437, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k3860437.jpg'}, {'end': 3993.268, 'src': 'embed', 'start': 3964.6, 'weight': 2, 'content': [{'end': 3968.744, 'text': 'okay?. and using the training set, you build your model.', 'start': 3964.6, 'duration': 4.144}, {'end': 3972.333, 'text': 'that means you practice and then build your mental model.', 'start': 3968.744, 'duration': 3.589}, {'end': 3979.345, 'text': 'okay, and once you find the model, you evaluate that model, how good that model is, using the testing dataset.', 'start': 3972.333, 'duration': 7.012}, {'end': 3987.707, 'text': 'In the testing datasets, since we derived it from the original data, we also have the real answer associated with it.', 'start': 3980.385, 'duration': 7.322}, {'end': 3993.268, 'text': 'And then the model is gonna predict something and then you can check how well you are doing.', 'start': 3988.287, 'duration': 4.981}], 'summary': 'Using training and testing datasets, you build and evaluate a predictive model for real-world applications.', 'duration': 28.668, 'max_score': 3964.6, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k3964600.jpg'}, {'end': 4073.434, 'src': 'embed', 'start': 4034.42, 'weight': 3, 'content': [{'end': 4043.925, 'text': "So if I look at it, N row of diabetes, that's my original data, total of 768 records.", 'start': 4034.42, 'duration': 9.505}, {'end': 4048.207, 'text': 'But then I randomly split into training and testing.', 'start': 4044.525, 'duration': 3.682}, {'end': 4057.761, 'text': "Now if I see N row of diabetes train, That's 529 records and test 239 records.", 'start': 4048.647, 'duration': 9.114}, {'end': 4064.206, 'text': 'So randomly, I divided the 768 dataset into two parts.', 'start': 4058.442, 'duration': 5.764}, {'end': 4069.391, 'text': 'One containing 529, another containing 239.', 'start': 4064.967, 'duration': 4.424}, {'end': 4073.434, 'text': "And then I'm going to build a model using the training dataset.", 'start': 4069.391, 'duration': 4.043}], 'summary': 'Original data has 768 records, training set has 529 and testing set has 239 records.', 'duration': 39.014, 'max_score': 4034.42, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k4034420.jpg'}, {'end': 4219.564, 'src': 'heatmap', 'start': 4107.634, 'weight': 4, 'content': [{'end': 4109.694, 'text': 'RPART stands for recursive partitioning.', 'start': 4107.634, 'duration': 2.06}, {'end': 4116.416, 'text': 'And in that particular R part, I have a function called R part again.', 'start': 4111.112, 'duration': 5.304}, {'end': 4124.962, 'text': "So that's the function which can learn a decision tree from a given training dataset, according to some measure, not exactly entropy,", 'start': 4116.935, 'duration': 8.027}, {'end': 4126.582, 'text': 'but some other variant of it.', 'start': 4124.962, 'duration': 1.62}, {'end': 4132.587, 'text': "And I need to tell what is my column on which I'm making predictions.", 'start': 4127.783, 'duration': 4.804}, {'end': 4135.386, 'text': 'in this case, has risk diabetes.', 'start': 4133.225, 'duration': 2.161}, {'end': 4137.627, 'text': 'and what is the data using which?', 'start': 4135.386, 'duration': 2.241}, {'end': 4144.97, 'text': "I'm training my model, and there are a bunch of other properties as well, other parameters that the function can take.", 'start': 4137.627, 'duration': 7.343}, {'end': 4153.633, 'text': "but at the minimum you need to give this what's the data from which you are learning and what's the column that you are trying to predict?", 'start': 4144.97, 'duration': 8.663}, {'end': 4164.078, 'text': "so that's the model and you can print out that model and it looks like this So at the root node you have all the 529 records.", 'start': 4153.633, 'duration': 10.445}, {'end': 4168.361, 'text': 'And then first you wanna check plasma glucose concentration.', 'start': 4164.919, 'duration': 3.442}, {'end': 4177.827, 'text': 'If it is less than 154.5, then you check body mass index, less than 26 or greater than 26, like that.', 'start': 4168.801, 'duration': 9.026}, {'end': 4184.74, 'text': "So it's like a tree structure, but we can actually plot it as a tree and then visualize it as well.", 'start': 4178.975, 'duration': 5.765}, {'end': 4189.283, 'text': "That's the other two things I'm doing here.", 'start': 4185.801, 'duration': 3.482}, {'end': 4190.564, 'text': 'It looks like that.', 'start': 4189.844, 'duration': 0.72}, {'end': 4195.568, 'text': "I'm not actually explaining individual syntax or anything.", 'start': 4191.765, 'duration': 3.803}, {'end': 4197.329, 'text': "I'm just telling you what we are trying to do.", 'start': 4195.588, 'duration': 1.741}, {'end': 4199.831, 'text': "So that's the decision tree that we constructed.", 'start': 4197.809, 'duration': 2.022}, {'end': 4202.557, 'text': 'what is the decision tree telling us?', 'start': 4200.256, 'duration': 2.301}, {'end': 4205.638, 'text': 'first, it is asking me to check the glucose concentration.', 'start': 4202.557, 'duration': 3.081}, {'end': 4213.222, 'text': 'it figured out that glucose concentration is the most important thing to make a prediction of diabetes.', 'start': 4205.638, 'duration': 7.584}, {'end': 4215.202, 'text': 'in this case, okay.', 'start': 4213.222, 'duration': 1.98}, {'end': 4219.564, 'text': "so I'm checking glucose concentration, which is probably intuitive, also right.", 'start': 4215.202, 'duration': 4.362}], 'summary': 'Using rpart to construct decision tree for diabetes prediction based on glucose concentration.', 'duration': 111.93, 'max_score': 4107.634, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k4107634.jpg'}], 'start': 3725.285, 'title': 'Decision tree model for diabetes prediction', 'summary': 'Introduces data science techniques and demonstrates the use of r language to analyze a diabetes dataset. it outlines the process of building and evaluating a decision tree model for predicting the risk of diabetes in women patients based on various features. additionally, it highlights the process of building and testing a decision tree model for diabetes prediction using a dataset of 768 records, emphasizing the importance of glucose concentration and age in making predictions.', 'chapters': [{'end': 4012.775, 'start': 3725.285, 'title': 'Data science techniques and decision tree model', 'summary': 'Introduces essential data science techniques, demonstrates the use of r language to analyze a diabetes dataset, and outlines the process of building and evaluating a decision tree model for predicting the risk of diabetes in women patients based on various features.', 'duration': 287.49, 'highlights': ['The dataset contains information about women patients in a diabetic study, with features including pregnancy frequency, glucose concentration, blood pressure, insulin level, and body mass index. The dataset contains information about women patients in a diabetic study, including features such as pregnancy frequency, glucose concentration, blood pressure, insulin level, and body mass index.', 'The goal is to build a decision tree model to predict the risk of diabetes in patients, which can help in taking preventive actions based on the predicted risk. The goal is to build a decision tree model to predict the risk of diabetes in patients, enabling the implementation of preventive actions based on the predicted risk.', "The process of building and evaluating the model involves dividing the dataset into a training set and a testing set to practice and evaluate the model's accuracy. The process involves dividing the dataset into a training set and a testing set, practicing on the training set, and evaluating the model's accuracy using the testing set."]}, {'end': 4286.567, 'start': 4012.775, 'title': 'Decision tree model for diabetes prediction', 'summary': 'Highlights the process of building and testing a decision tree model for diabetes prediction using a dataset of 768 records, where the training set contains 529 records and the test set contains 239 records, and emphasizes the importance of glucose concentration and age in making predictions.', 'duration': 273.792, 'highlights': ['The dataset is divided into a training set of 529 records and a test set of 239 records for building and testing the decision tree model for diabetes prediction. The original dataset of 768 records is randomly split into a training set of 529 records and a test set of 239 records, highlighting the division process.', 'The decision tree model is built using the training dataset, with a focus on glucose concentration and age as key predictors of diabetes risk. The decision tree model utilizes glucose concentration and age as the primary predictors for identifying diabetes risk, emphasizing the significance of these variables in making accurate predictions.', 'The significance of glucose concentration and age in predicting diabetes risk is emphasized, aligning with general knowledge of diabetes indicators. The decision tree model underscores the importance of glucose concentration and age in predicting diabetes risk, aligning with common knowledge about the relevance of these factors as diabetes indicators.']}], 'duration': 561.282, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k3725285.jpg', 'highlights': ['The dataset contains information about women patients in a diabetic study, including features such as pregnancy frequency, glucose concentration, blood pressure, insulin level, and body mass index.', 'The goal is to build a decision tree model to predict the risk of diabetes in patients, enabling the implementation of preventive actions based on the predicted risk.', "The process involves dividing the dataset into a training set and a testing set, practicing on the training set, and evaluating the model's accuracy using the testing set.", 'The original dataset of 768 records is randomly split into a training set of 529 records and a test set of 239 records, highlighting the division process.', 'The decision tree model utilizes glucose concentration and age as the primary predictors for identifying diabetes risk, emphasizing the significance of these variables in making accurate predictions.', 'The decision tree model underscores the importance of glucose concentration and age in predicting diabetes risk, aligning with common knowledge about the relevance of these factors as diabetes indicators.']}, {'end': 4884.252, 'segs': [{'end': 4317.856, 'src': 'embed', 'start': 4287.067, 'weight': 2, 'content': [{'end': 4290.029, 'text': 'So a bunch of decision tree that it created.', 'start': 4287.067, 'duration': 2.962}, {'end': 4294.39, 'text': 'Now this is the model that we built from the data.', 'start': 4290.888, 'duration': 3.502}, {'end': 4296.711, 'text': 'Now we need to evaluate it.', 'start': 4295.13, 'duration': 1.581}, {'end': 4299.433, 'text': 'How good this model is.', 'start': 4298.352, 'duration': 1.081}, {'end': 4302.675, 'text': 'And for that, we are going to use our test data.', 'start': 4299.853, 'duration': 2.822}, {'end': 4305.476, 'text': 'The portion of the data set that we kept aside.', 'start': 4303.235, 'duration': 2.241}, {'end': 4313.532, 'text': "And what are we going to do? We're going to use this model, apply it on testing data, and then get some predictions.", 'start': 4306.826, 'duration': 6.706}, {'end': 4317.856, 'text': 'What are the predictions according to this particular model?', 'start': 4314.213, 'duration': 3.643}], 'summary': 'Evaluating a decision tree model using test data for predictions.', 'duration': 30.789, 'max_score': 4287.067, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k4287067.jpg'}, {'end': 4472.467, 'src': 'heatmap', 'start': 4346.311, 'weight': 0, 'content': [{'end': 4354.056, 'text': 'But then I also know the actual values of this records, right? That is basically dollar has risk diabetes.', 'start': 4346.311, 'duration': 7.745}, {'end': 4355.877, 'text': 'These are the actual values.', 'start': 4354.376, 'duration': 1.501}, {'end': 4358.099, 'text': 'So these are my predicted values.', 'start': 4356.598, 'duration': 1.501}, {'end': 4359.68, 'text': 'These are my actual values.', 'start': 4358.439, 'duration': 1.241}, {'end': 4363.703, 'text': "So it's like a answer sheet, right? This is the answer key.", 'start': 4360.32, 'duration': 3.383}, {'end': 4365.144, 'text': 'S no, S no.', 'start': 4364.223, 'duration': 0.921}, {'end': 4368.446, 'text': 'And these are the answers given by a student.', 'start': 4365.844, 'duration': 2.602}, {'end': 4370.047, 'text': 'Now we can match.', 'start': 4369.126, 'duration': 0.921}, {'end': 4373.349, 'text': 'We can say how many he got right, how many he got wrong.', 'start': 4370.707, 'duration': 2.642}, {'end': 4375.911, 'text': "That's the accuracy or percentage.", 'start': 4373.909, 'duration': 2.002}, {'end': 4381.375, 'text': 'And to do that also, there is a notion of a confusion matrix.', 'start': 4377.352, 'duration': 4.023}, {'end': 4384.497, 'text': "It's like this.", 'start': 4383.957, 'duration': 0.54}, {'end': 4388.921, 'text': 'The actual answers are here, the predicted answers are here.', 'start': 4385.398, 'duration': 3.523}, {'end': 4402.871, 'text': 'So we got 123 and 47, meaning there are 123 questions or test records for which the model said no and also the actual answer is also no.', 'start': 4389.862, 'duration': 13.009}, {'end': 4410.018, 'text': 'And then there are 47 questions where the model said yes and the actual answer is also yes.', 'start': 4403.552, 'duration': 6.466}, {'end': 4420.928, 'text': 'So 123 plus 47 are the right number of questions on which the model did, these are the number of questions on which the model did write.', 'start': 4410.959, 'duration': 9.969}, {'end': 4433.022, 'text': 'So my accuracy is 123 plus 47 divided by the total 239, which is, if you compute it, it becomes 0.7113.', 'start': 4422.014, 'duration': 11.008}, {'end': 4435.744, 'text': 'So 71.13%.', 'start': 4433.022, 'duration': 2.722}, {'end': 4442.649, 'text': 'So that means if I use this model in production, I would get roughly 71% accuracy.', 'start': 4435.744, 'duration': 6.905}, {'end': 4446.892, 'text': 'Out of 100 patients I make prediction, 71% I get right.', 'start': 4443.149, 'duration': 3.743}, {'end': 4449.533, 'text': "The remaining, I'm gonna make some mistakes.", 'start': 4447.592, 'duration': 1.941}, {'end': 4455.28, 'text': "Make sense, everyone? So that's the accuracy.", 'start': 4450.654, 'duration': 4.626}, {'end': 4458.081, 'text': 'And similarly, there are a bunch of other measures as well.', 'start': 4455.7, 'duration': 2.381}, {'end': 4462.303, 'text': 'Like these are all the different other measures that people may be interested in.', 'start': 4458.461, 'duration': 3.842}, {'end': 4466.904, 'text': 'Like confidence interval is one thing, sensitivity, specificity.', 'start': 4462.943, 'duration': 3.961}, {'end': 4468.205, 'text': 'There are a bunch of other things.', 'start': 4467.025, 'duration': 1.18}, {'end': 4472.467, 'text': 'Each one has a different flavor and the type of information that it captures.', 'start': 4468.305, 'duration': 4.162}], 'summary': 'Model accuracy is 71.13%, predicting 71% of patients correctly in production.', 'duration': 82.605, 'max_score': 4346.311, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k4346311.jpg'}, {'end': 4698.026, 'src': 'embed', 'start': 4667.498, 'weight': 4, 'content': [{'end': 4675.101, 'text': 'the patient is fine actually, but the model said oh, this patient has a risk of diabetes.', 'start': 4667.498, 'duration': 7.603}, {'end': 4676.641, 'text': "so it's a false alarm.", 'start': 4675.101, 'duration': 1.54}, {'end': 4678.462, 'text': "it's called as false positive.", 'start': 4676.641, 'duration': 1.821}, {'end': 4681.123, 'text': "okay, so that's 24 of those.", 'start': 4678.462, 'duration': 2.661}, {'end': 4690.166, 'text': 'and then there are 45 patients, or 45 questions for which the model said no, but the actual said yes.', 'start': 4681.123, 'duration': 9.043}, {'end': 4698.026, 'text': 'that means the patients indeed has a risk of diabetes, But our model said no, no, no, these guys are fine.', 'start': 4690.166, 'duration': 7.86}], 'summary': '24 false positive cases and 45 misclassified diabetes risk patients.', 'duration': 30.528, 'max_score': 4667.498, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k4667498.jpg'}, {'end': 4837.373, 'src': 'embed', 'start': 4812.466, 'weight': 3, 'content': [{'end': 4818.248, 'text': 'but typically you have different costs for different mistakes and, depending on your application,', 'start': 4812.466, 'duration': 5.782}, {'end': 4826.471, 'text': 'you may choose a different measure that gives you higher priority for the high cost mistakes and lower priority for the low cost mistakes.', 'start': 4818.248, 'duration': 8.223}, {'end': 4832.953, 'text': "okay, so that's just one flavor, but then there are other aspects to these as well.", 'start': 4826.471, 'duration': 6.482}, {'end': 4833.313, 'text': 'make sense?', 'start': 4832.953, 'duration': 0.36}, {'end': 4837.373, 'text': "Okay, so that's pretty much about what I had to say in the webinar.", 'start': 4833.891, 'duration': 3.482}], 'summary': 'Different costs for mistakes can be prioritized based on application needs. other aspects also play a role.', 'duration': 24.907, 'max_score': 4812.466, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k4812466.jpg'}], 'start': 4287.067, 'title': 'Model evaluation and improvement', 'summary': 'Discusses the process of building and evaluating a model, including measures like accuracy, false positives, and false negatives, and the model achieving approximately 71.13% accuracy in predicting diabetes risk.', 'chapters': [{'end': 4455.28, 'start': 4287.067, 'title': 'Model evaluation and accuracy calculation', 'summary': 'Explains the process of evaluating a decision tree model using test data and calculating its accuracy using a confusion matrix, with the model achieving approximately 71.13% accuracy in predicting diabetes risk.', 'duration': 168.213, 'highlights': ['The model achieves approximately 71.13% accuracy in predicting diabetes risk. The accuracy of the model is calculated using a confusion matrix, with 71.13% accuracy achieved in predicting diabetes risk out of 100 patients.', "The confusion matrix is used to determine the accuracy of the model. The confusion matrix displays the number of correct predictions made by the model, with 123 correct 'no' predictions and 47 correct 'yes' predictions out of a total of 239 test records.", "The process of evaluating the model involves applying it to the test data and comparing its predictions with the actual results. The model is applied to the test data to obtain predictions, which are then compared with the actual values to assess the model's performance."]}, {'end': 4884.252, 'start': 4455.7, 'title': 'Model evaluation and improvement', 'summary': 'Discusses the process of building and evaluating a model, including measures like accuracy, false positives, and false negatives, and the importance of considering different costs for different types of mistakes when evaluating a model for a specific application.', 'duration': 428.552, 'highlights': ['The chapter discusses the importance of considering different costs for different types of mistakes when evaluating a model for a specific application. Different types of mistakes have different cost associated with it in your business, and depending on your application, you may choose a different measure that gives you higher priority for the high cost mistakes and lower priority for the low cost mistakes.', 'The chapter explains the concept of false positives and false negatives in model evaluation. The model made some mistakes. There are 24 false positives and 45 false negatives, each with different costs and impacts in a business context.', 'The chapter emphasizes the need to go beyond accuracy and consider other measures like sensitivity, specificity, and confidence intervals when evaluating a model. Besides accuracy, the chapter mentions the importance of measures like sensitivity, specificity, and confidence intervals, and highlights the need to prioritize different types of mistakes based on their costs in a business context.']}], 'duration': 597.185, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/opQ49Xr748k/pics/opQ49Xr748k4287067.jpg', 'highlights': ['The model achieves approximately 71.13% accuracy in predicting diabetes risk using a confusion matrix.', "The confusion matrix displays 123 correct 'no' predictions and 47 correct 'yes' predictions out of a total of 239 test records.", 'The process of evaluating the model involves applying it to the test data and comparing its predictions with the actual results.', 'The chapter discusses the importance of considering different costs for different types of mistakes when evaluating a model for a specific application.', 'The chapter explains the concept of false positives and false negatives in model evaluation, with 24 false positives and 45 false negatives.', 'The chapter emphasizes the need to go beyond accuracy and consider other measures like sensitivity, specificity, and confidence intervals when evaluating a model.']}], 'highlights': ['The model achieves approximately 71.13% accuracy in predicting diabetes risk using a confusion matrix.', "The confusion matrix displays 123 correct 'no' predictions and 47 correct 'yes' predictions out of a total of 239 test records.", 'The process of evaluating the model involves applying it to the test data and comparing its predictions with the actual results.', 'The chapter emphasizes the need to go beyond accuracy and consider other measures like sensitivity, specificity, and confidence intervals when evaluating a model.', 'The decision tree model utilizes glucose concentration and age as the primary predictors for identifying diabetes risk, emphasizing the significance of these variables in making accurate predictions.', 'The decision tree model underscores the importance of glucose concentration and age in predicting diabetes risk, aligning with common knowledge about the relevance of these factors as diabetes indicators.', 'The original dataset of 768 records is randomly split into a training set of 529 records and a test set of 239 records, highlighting the division process.', 'The goal is to build a decision tree model to predict the risk of diabetes in patients, enabling the implementation of preventive actions based on the predicted risk.', 'The dataset contains information about women patients in a diabetic study, including features such as pregnancy frequency, glucose concentration, blood pressure, insulin level, and body mass index.', 'Different data science algorithms are compared to tools in a toolbox, each suited for specific problems.', 'Fundamental techniques like decision trees, linear regression, and Naive Bayes are essential for data scientists.', 'Entropy measures uncertainty in data, aiding attribute selection in machine learning.', "Entropy's role in decision making and data partitioning is explained, emphasizing its impact on predictability.", 'The selection of algorithms is based on performance measures like accuracy, precision, and recall.', 'The decision tree splits the data based on weather outlook, humidity, and wind values, leading to data-driven play or not play decisions.', 'The decision tree is constructed based on historical data of weather conditions and play decisions, with a focus on analyzing feature values and their distribution to make predictive decisions.', 'Decision tree uses outlook, humidity, and wind features to predict outdoor play decisions based on historical training data, emphasizing the use of historical training data.', 'The decision tree algorithm involves making decisions based on different paths and outcomes, illustrated through examples such as email classification and credit risk analysis.', 'The decision tree algorithm can be used for real-world applications like credit risk analysis when assessing loan applicants, involving analyzing factors such as income and credit history to determine the risk level of an applicant.', 'Machine learning applications are prevalent in everyday life, including personalized advertisements, email filters, and product recommendations on e-commerce platforms.', 'Feedback loop and continuous learning are fundamental concepts in both human and machine learning.', 'Machine learning is akin to human learning, where exposure to examples enables the brain to distinguish between different classes of data points.', 'A comparison is drawn between traditional programming and machine learning approaches in developing face recognition software.', 'Alan Turing and the birth of artificial intelligence are referenced, emphasizing the aspiration to make machines smarter.', 'Machine learning enables computers to learn without explicit programming.', 'The webinar delves into fundamental aspects of machine learning, with a specific focus on predictive analytics and decision trees, providing valuable insights to the audience.', "Machine learning's diverse applications in retail, such as demand forecasting and supply chain management, demonstrate its impact on business operations.", "Sriraj's extensive experience and expertise in big data and analytics, with a PhD in the field, provides credibility to the webinar."]}