title
Machine Learning Tutorial Python - 18: K nearest neighbors classification with python code

description
In this video we will understand how K nearest neighbors algorithm work. Then write python code using sklearn library to build a knn (K nearest neighbors) model. The end, I have an exercise for you to practice concepts you learnt in this video. Code: https://github.com/codebasics/py/blob/master/ML/17_knn_classification/knn_classification_tutorial.ipynb Exercise: https://github.com/codebasics/py/blob/master/ML/17_knn_classification/knn_exercise.md ⭐️ Timestamps ⭐️ 00:00 Theory 03:51 Coding 14:09 Exercise Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses. Machine learning tutorial playlist for beginners: https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw 🌎 My Website For Video Courses: https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description Need help building software or data analytics and AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website. 🎥 Codebasics Hindi channel: https://www.youtube.com/channel/UCTmFBhuhMibVoSfYom1uXEg #️⃣ Social Media #️⃣ 🔗 Discord: https://discord.gg/r42Kbuk 📸 Dhaval's Personal Instagram: https://www.instagram.com/dhavalsays/ 📸 Instagram: https://www.instagram.com/codebasicshub/ 🔊 Facebook: https://www.facebook.com/codebasicshub 📱 Twitter: https://twitter.com/codebasicshub 📝 Linkedin (Personal): https://www.linkedin.com/in/dhavalsays/ 📝 Linkedin (Codebasics): https://www.linkedin.com/company/codebasics/ ❗❗ DISCLAIMER: All opinions expressed in this video are of my own and not that of my employers'.

detail
{'title': 'Machine Learning Tutorial Python - 18: K nearest neighbors classification with python code', 'heatmap': [{'end': 310.979, 'start': 291.865, 'weight': 0.855}, {'end': 414.922, 'start': 394.785, 'weight': 0.71}, {'end': 697.803, 'start': 675.619, 'weight': 1}], 'summary': 'Tutorial covers k-nearest-neighbor (knn) classification, explaining the algorithm, python implementation, model building, and application for classifying iris flowers. it emphasizes the process of determining the value of k, the impact of different k values on classification, and model evaluation using confusion matrix and classification report, with a specific example achieving a high accuracy score.', 'chapters': [{'end': 50.676, 'segs': [{'end': 50.676, 'src': 'embed', 'start': 0.834, 'weight': 0, 'content': [{'end': 6.279, 'text': "In this machine learning tutorial, we'll look into what is k-nearest-neighbor classification.", 'start': 0.834, 'duration': 5.445}, {'end': 13.746, 'text': "We'll go over some theory, then we'll write Python code, we'll build a model, and then you will have an exercise to work on.", 'start': 6.319, 'duration': 7.427}, {'end': 17.47, 'text': "Let's say you are doing a classification for iris flowers.", 'start': 14.507, 'duration': 2.963}, {'end': 22.932, 'text': 'Here I have a picture of versicolor flower, which is one of the three types.', 'start': 18.769, 'duration': 4.163}, {'end': 32.998, 'text': 'And based on a sepal width and height, you can actually figure out which of the three flowers category it is in.', 'start': 23.992, 'duration': 9.006}, {'end': 39.383, 'text': 'So we are classifying an iris flower into one of the three classes, setosa, versicolor, virginica.', 'start': 33.339, 'duration': 6.044}, {'end': 50.676, 'text': 'Based on sample width and height, you can plot sample width and height in this kind of 2D scatter plot.', 'start': 40.083, 'duration': 10.593}], 'summary': 'Tutorial on k-nearest-neighbor classification with python code and iris flower classification', 'duration': 49.842, 'max_score': 0.834, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM834.jpg'}], 'start': 0.834, 'title': 'K-nearest-neighbor classification', 'summary': 'Delves into k-nearest-neighbor classification, covering theory, python code implementation, model building, and application for classifying iris flowers into three categories based on sepal width and height.', 'chapters': [{'end': 50.676, 'start': 0.834, 'title': 'K-nearest-neighbor classification', 'summary': 'Explains k-nearest-neighbor classification, including theory, python code implementation, model building, and application for classifying iris flowers into three categories based on sepal width and height.', 'duration': 49.842, 'highlights': ['The chapter covers the theory, Python code implementation, model building, and exercise related to k-nearest-neighbor classification, providing a comprehensive learning experience.', 'It demonstrates the application of k-nearest-neighbor classification for classifying iris flowers into three categories – setosa, versicolor, virginica – based on sepal width and height.']}], 'duration': 49.842, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM834.jpg', 'highlights': ['Demonstrates k-nearest-neighbor classification for classifying iris flowers into three categories based on sepal width and height.', 'Covers theory, Python code implementation, model building, and exercise related to k-nearest-neighbor classification.']}, {'end': 205.826, 'segs': [{'end': 110.655, 'src': 'embed', 'start': 79.41, 'weight': 1, 'content': [{'end': 85.27, 'text': 'because it is more near to this cluster And KNN works just like that.', 'start': 79.41, 'duration': 5.86}, {'end': 91.511, 'text': 'In KNN, you first figure out what is the value of my K.', 'start': 85.63, 'duration': 5.881}, {'end': 94.172, 'text': 'And you can figure out the value of K by trial and error.', 'start': 91.511, 'duration': 2.661}, {'end': 97.713, 'text': 'There is just no specific rule to it.', 'start': 94.192, 'duration': 3.521}, {'end': 101.233, 'text': 'Usually people use five, but you can change it.', 'start': 97.813, 'duration': 3.42}, {'end': 104.754, 'text': "So let's say here I use K is equal to three.", 'start': 102.313, 'duration': 2.441}, {'end': 110.655, 'text': 'And this means I need to figure out the most nearby three data points.', 'start': 105.914, 'duration': 4.741}], 'summary': 'Knn algorithm finds k nearest data points, typically using a value of 5 for k, but it can be adjusted by trial and error.', 'duration': 31.245, 'max_score': 79.41, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM79410.jpg'}, {'end': 182.251, 'src': 'embed', 'start': 158.32, 'weight': 0, 'content': [{'end': 166.07, 'text': 'Well then you have a problem, because your total number of data point in Virginica class are very less.', 'start': 158.32, 'duration': 7.75}, {'end': 172.698, 'text': 'So now it is misclassifying this yellow data point saying that this is versicolor.', 'start': 166.991, 'duration': 5.707}, {'end': 180.07, 'text': 'Because the rule is you figure out the most nearby K data point and you take the maximum count.', 'start': 173.567, 'duration': 6.503}, {'end': 182.251, 'text': 'So here K is 20.', 'start': 180.75, 'duration': 1.501}], 'summary': 'Misclassification due to low data points in virginica class, k=20.', 'duration': 23.931, 'max_score': 158.32, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM158320.jpg'}], 'start': 51.136, 'title': 'K nearest neighbor algorithm', 'summary': 'Explains the k nearest neighbor (knn) algorithm, detailing its workings, the process of determining the value of k, and the impact of different k values on classification, emphasizing the importance of choosing an appropriate k value to avoid misclassification.', 'chapters': [{'end': 205.826, 'start': 51.136, 'title': 'K nearest neighbor algorithm', 'summary': 'Explains the k nearest neighbor (knn) algorithm, detailing how it works, the process of determining the value of k, and the impact of different k values on classification, emphasizing the importance of choosing an appropriate k value to avoid misclassification.', 'duration': 154.69, 'highlights': ['In KNN, the value of K is determined by trial and error, with five being the usual choice, and the algorithm works by identifying the most nearby K data points to classify a new data point, exemplified through the process of determining the class of a yellow data point using different K values.', "The KNN algorithm's classification accuracy is influenced by the value of K, as demonstrated by the misclassification of a yellow data point as versicolor instead of virginica when K is set to 20, emphasizing the significance of choosing an appropriate K value to ensure accurate classification.", "The KNN algorithm's simplicity lies in its process of classifying a new data point based on the category of the most nearby K data points, as illustrated by the determination of the category of a new data point based on the highest count of nearby data points belonging to a specific category."]}], 'duration': 154.69, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM51136.jpg', 'highlights': ["The KNN algorithm's classification accuracy is influenced by the value of K, as demonstrated by the misclassification of a yellow data point as versicolor instead of virginica when K is set to 20, emphasizing the significance of choosing an appropriate K value to ensure accurate classification.", 'In KNN, the value of K is determined by trial and error, with five being the usual choice, and the algorithm works by identifying the most nearby K data points to classify a new data point, exemplified through the process of determining the class of a yellow data point using different K values.', "The KNN algorithm's simplicity lies in its process of classifying a new data point based on the category of the most nearby K data points, as illustrated by the determination of the category of a new data point based on the highest count of nearby data points belonging to a specific category."]}, {'end': 529.246, 'segs': [{'end': 310.979, 'src': 'heatmap', 'start': 205.826, 'weight': 0, 'content': [{'end': 211.692, 'text': 'style and error depends on your situation, depends on your, your, your training data set and so on.', 'start': 205.826, 'duration': 5.866}, {'end': 213.414, 'text': "let's write python code now.", 'start': 211.692, 'duration': 1.722}, {'end': 219.378, 'text': 'One thing I would like to clarify is that here we had only two features sepal width and sepal height.', 'start': 214.275, 'duration': 5.103}, {'end': 222.779, 'text': 'But KNN works equally if there are more than two features.', 'start': 219.458, 'duration': 3.321}, {'end': 226.681, 'text': 'There could be n number of features, and KNN will just work fine.', 'start': 222.799, 'duration': 3.882}, {'end': 228.622, 'text': "I mean, as a human, we can't visualize it.", 'start': 226.741, 'duration': 1.881}, {'end': 230.984, 'text': 'But mathematically, it should just work OK.', 'start': 229.003, 'duration': 1.981}, {'end': 235.605, 'text': 'We are going to use same iris data flower data set for our coding.', 'start': 231.764, 'duration': 3.841}, {'end': 246.689, 'text': 'If you go to YouTube and search for support vector machine Python, I have this SVM video in which I used iris flower data set.', 'start': 236.285, 'duration': 10.404}, {'end': 249.83, 'text': "So I'm going to use some of this same code here.", 'start': 247.169, 'duration': 2.661}, {'end': 259.132, 'text': 'So if you go here and look at the code and if you have followed this video, you would already be familiar with this code.', 'start': 250.47, 'duration': 8.662}, {'end': 266.736, 'text': 'So I will use the same code till the point that I create a classifier So same code is here.', 'start': 260.033, 'duration': 6.703}, {'end': 279.82, 'text': 'Basically, you are loading iris dataset from sklearn datasets, then obviously the feature names are sepal width and length, petal width and length.', 'start': 267.036, 'duration': 12.784}, {'end': 282.561, 'text': 'sepal and petal are two different flower leaves,', 'start': 279.82, 'duration': 2.741}, {'end': 291.825, 'text': "like the components of the flower and And based on these four features we determine whether it's setosa, versicolor or virginica.", 'start': 282.561, 'duration': 9.264}, {'end': 293.866, 'text': 'So our target classes are three.', 'start': 291.865, 'duration': 2.001}, {'end': 299.128, 'text': 'I have loaded iris.data in a data frame.', 'start': 295.626, 'duration': 3.502}, {'end': 304.01, 'text': "And then I'm adding a new column called target.", 'start': 300.548, 'duration': 3.462}, {'end': 306.898, 'text': 'in the same data frame.', 'start': 305.037, 'duration': 1.861}, {'end': 309.378, 'text': 'so you see, now my data frame has five columns.', 'start': 306.898, 'duration': 2.48}, {'end': 310.979, 'text': 'four are my features.', 'start': 309.378, 'duration': 1.601}], 'summary': 'Knn works with any number of features, demonstrated using iris flower dataset in python.', 'duration': 103.552, 'max_score': 205.826, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM205826.jpg'}, {'end': 436.983, 'src': 'heatmap', 'start': 394.785, 'weight': 5, 'content': [{'end': 398.146, 'text': 'So these are the two kind of clusters that we have.', 'start': 394.785, 'duration': 3.361}, {'end': 407.975, 'text': 'Looking at all these clusters, we get immediate intuition that we can apply KNN classifier here.', 'start': 399.006, 'duration': 8.969}, {'end': 411.959, 'text': "All right, and I'm doing train test split, usual thing.", 'start': 407.975, 'duration': 3.984}, {'end': 414.922, 'text': 'if you have followed my videos, you would know what this is.', 'start': 411.959, 'duration': 2.963}, {'end': 417.505, 'text': 'So we are dividing our samples into training and test.', 'start': 414.922, 'duration': 2.583}, {'end': 419.947, 'text': 'data set and training set now has 120 samples.', 'start': 417.505, 'duration': 2.442}, {'end': 429.256, 'text': 'train test has Sorry, test set has 30 data sample.', 'start': 419.947, 'duration': 9.309}, {'end': 434.981, 'text': 'Alright, now comes the most interesting part, which is creating a K nearest neighbor classifier.', 'start': 430.177, 'duration': 4.804}, {'end': 436.983, 'text': 'Google is your friend.', 'start': 436.082, 'duration': 0.901}], 'summary': 'Data set has 120 training samples and 30 test samples. applying knn classifier.', 'duration': 29.008, 'max_score': 394.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM394785.jpg'}, {'end': 497.314, 'src': 'embed', 'start': 468.403, 'weight': 4, 'content': [{'end': 472.565, 'text': 'okay, what are the parameters in the classifier?', 'start': 468.403, 'duration': 4.162}, {'end': 473.726, 'text': 'obvious one is neighbors.', 'start': 472.565, 'duration': 1.161}, {'end': 475.267, 'text': 'this is basically the value of K.', 'start': 473.726, 'duration': 1.541}, {'end': 479.252, 'text': "So I'm just going with you know three.", 'start': 477.25, 'duration': 2.002}, {'end': 481.956, 'text': 'And there are other values.', 'start': 480.854, 'duration': 1.102}, {'end': 484.519, 'text': 'You can read the documentation.', 'start': 483.317, 'duration': 1.202}, {'end': 486.341, 'text': "I'm going to keep everything else default.", 'start': 484.539, 'duration': 1.802}, {'end': 487.662, 'text': 'This is a beginner tutorial.', 'start': 486.461, 'duration': 1.201}, {'end': 492.528, 'text': "But metric Minkowski, what is that? Let's read.", 'start': 488.563, 'duration': 3.965}, {'end': 497.314, 'text': 'So metric.', 'start': 496.092, 'duration': 1.222}], 'summary': 'Tutorial covers classifier parameters, focusing on k-value with a beginner approach.', 'duration': 28.911, 'max_score': 468.403, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM468403.jpg'}], 'start': 205.826, 'title': 'Knn algorithm for iris flower classification', 'summary': 'Explores the implementation of knn algorithm in python for iris flower classification, using multiple features from the iris flower dataset, creating a knn classifier with k=3, and splitting the dataset into training and test sets with 120 and 30 samples respectively.', 'chapters': [{'end': 279.82, 'start': 205.826, 'title': 'Understanding knn algorithm for multi-feature data', 'summary': 'Explores the implementation of knn algorithm using python, emphasizing its adaptability to multiple features, as demonstrated with the iris flower dataset, and refers to a previous svm video on youtube for additional reference.', 'duration': 73.994, 'highlights': ['The KNN algorithm works effectively with multiple features, accommodating n number of features, as demonstrated with the iris flower dataset.', 'The chapter mentions the use of Python for implementing the KNN algorithm and refers to a YouTube video on support vector machine for additional context and code reference.', 'The code for loading the iris dataset from sklearn datasets and defining the feature names, including sepal width and length, petal width and length, is discussed in detail.']}, {'end': 529.246, 'start': 279.82, 'title': 'Iris flower classification with knn', 'summary': 'Covers the classification of iris flowers into setosa, versicolor, and virginica based on four features, creating knn classifier with k=3, and splitting the dataset into training and test sets with 120 and 30 samples respectively.', 'duration': 249.426, 'highlights': ['The chapter covers the classification of iris flowers into setosa, versicolor, and virginica based on four features The target classes are setosa, versicolor, and virginica based on four features, with 50 setosa, 50 versicolor, and 50 virginica training samples.', 'Creating KNN classifier with K=3 The KNN classifier is created with K=3, using the default values for other parameters, and utilizing the Minkowski metric for computing Euclidean distance.', 'Splitting the dataset into training and test sets with 120 and 30 samples respectively The dataset is divided into a training set with 120 samples and a test set with 30 samples to train and evaluate the KNN classifier.']}], 'duration': 323.42, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM205826.jpg', 'highlights': ['The KNN algorithm accommodates n number of features, demonstrated with the iris flower dataset.', 'Python is used for implementing the KNN algorithm, with reference to a YouTube video on support vector machine for additional context and code reference.', 'Loading the iris dataset from sklearn datasets and defining the feature names, including sepal width and length, petal width and length, is discussed in detail.', 'The chapter covers the classification of iris flowers into setosa, versicolor, and virginica based on four features, with 50 samples for each class.', 'The KNN classifier is created with K=3, using the default values for other parameters, and utilizing the Minkowski metric for computing Euclidean distance.', 'The dataset is divided into a training set with 120 samples and a test set with 30 samples to train and evaluate the KNN classifier.']}, {'end': 941.24, 'segs': [{'end': 655.504, 'src': 'embed', 'start': 530.006, 'weight': 0, 'content': [{'end': 537.631, 'text': "I'm just going to use whatever is the default and simply call knn.fit method.", 'start': 530.006, 'duration': 7.625}, {'end': 546.036, 'text': 'So I created knn classifier, call knn fit on x train and y train.', 'start': 538.111, 'duration': 7.925}, {'end': 550.859, 'text': 'And my classifier is trained already.', 'start': 548.798, 'duration': 2.061}, {'end': 556.523, 'text': 'And first thing I do usually after training my classifier is compute the score.', 'start': 551.86, 'duration': 4.663}, {'end': 560.143, 'text': "So when you compute the score, you're giving X test.", 'start': 557.622, 'duration': 2.521}, {'end': 566.886, 'text': 'So using the more trained model, it will find out the prediction and then it will compare that with Y test.', 'start': 560.223, 'duration': 6.663}, {'end': 568.727, 'text': 'And it will tell you how accurate is your model.', 'start': 566.926, 'duration': 1.801}, {'end': 570.928, 'text': 'My model is so much accuracy.', 'start': 569.107, 'duration': 1.821}, {'end': 573.529, 'text': 'It got all prediction right.', 'start': 572.288, 'duration': 1.241}, {'end': 581.912, 'text': 'You can maybe change this value.', 'start': 575.31, 'duration': 6.602}, {'end': 585.534, 'text': "I mean, three is ideal, but I'm just changing the value just for fun.", 'start': 582.212, 'duration': 3.322}, {'end': 590.175, 'text': 'And see with different value of K you get different score.', 'start': 587.133, 'duration': 3.042}, {'end': 592.577, 'text': 'So whenever you are using KNN.', 'start': 590.215, 'duration': 2.362}, {'end': 597.861, 'text': 'Try to figure out best value of K for your use case.', 'start': 594.138, 'duration': 3.723}, {'end': 605.987, 'text': 'You can use grid search CV or K fold cross validation to figure out the optimal value of N neighbors.', 'start': 598.562, 'duration': 7.425}, {'end': 614.214, 'text': "For us the optimal value is 3 but I'm going to keep it 10 so that you know we can see some variation in our confusion matrix.", 'start': 606.648, 'duration': 7.566}, {'end': 615.815, 'text': 'So in the.', 'start': 615.515, 'duration': 0.3}, {'end': 618.788, 'text': 'previous videos.', 'start': 617.588, 'duration': 1.2}, {'end': 621.789, 'text': 'we have already plotted confusion metric.', 'start': 618.788, 'duration': 3.001}, {'end': 632.233, 'text': 'so confusion matrix tells you for which classes it got the prediction right, for which classes it did not get the prediction right.', 'start': 621.789, 'duration': 10.444}, {'end': 645.758, 'text': 'so to plot it you will import confusion matrix from sklearn matrix library and then what you do is you use knn for making prediction.', 'start': 632.233, 'duration': 13.525}, {'end': 652.803, 'text': 'So you give X taste, you find Y predicted and then in confusion matrix.', 'start': 647.062, 'duration': 5.741}, {'end': 655.504, 'text': 'There are two parameters.', 'start': 654.544, 'duration': 0.96}], 'summary': 'Trained knn classifier with 100% accuracy, evaluated different k values for optimal performance, and discussed confusion matrix.', 'duration': 125.498, 'max_score': 530.006, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM530006.jpg'}, {'end': 710.392, 'src': 'heatmap', 'start': 675.619, 'weight': 1, 'content': [{'end': 679.202, 'text': 'But for better visualization, we can use a seaborn library.', 'start': 675.619, 'duration': 3.583}, {'end': 681.144, 'text': 'We have used it in previous videos.', 'start': 679.643, 'duration': 1.501}, {'end': 686.81, 'text': "I'm not going to go over this code in detail, but it's the same exact grid.", 'start': 681.184, 'duration': 5.626}, {'end': 689.353, 'text': 'You are just having a little better visualization.', 'start': 687.13, 'duration': 2.223}, {'end': 694.981, 'text': 'anything on the diagonal are your correct prediction?', 'start': 690.678, 'duration': 4.303}, {'end': 697.803, 'text': 'this means this zero is setosa.', 'start': 694.981, 'duration': 2.822}, {'end': 700.205, 'text': 'this zero is setosa, and this is truth.', 'start': 697.803, 'duration': 2.402}, {'end': 701.886, 'text': 'this is prediction.', 'start': 700.205, 'duration': 1.681}, {'end': 710.392, 'text': 'what this is saying is 11 times, 11 times it was setosa and my model predicted it to be setosa.', 'start': 701.886, 'duration': 8.506}], 'summary': 'Seaborn library used for better visualization. model predicted setosa 11 times accurately.', 'duration': 34.773, 'max_score': 675.619, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM675619.jpg'}, {'end': 800.877, 'src': 'embed', 'start': 770.479, 'weight': 3, 'content': [{'end': 781.625, 'text': 'so from sklearn matrix you can import classification report And this classification report takes your.', 'start': 770.479, 'duration': 11.146}, {'end': 794.183, 'text': 'Why taste and why predicted? And you know, OK, the formatting is not great, so if you put print statement is going to show you little better result.', 'start': 782.766, 'duration': 11.417}, {'end': 799.154, 'text': 'And this is showing you Precision Recall FN score for each of the classes.', 'start': 795.689, 'duration': 3.465}, {'end': 800.877, 'text': 'This one class, two class and so on.', 'start': 799.275, 'duration': 1.602}], 'summary': 'The classification report from sklearn matrix displays precision, recall, and f1 score for each class.', 'duration': 30.398, 'max_score': 770.479, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM770479.jpg'}], 'start': 530.006, 'title': 'Knn classifier basics and model evaluation', 'summary': 'Covers training a knn classifier, computing accuracy, finding optimal k value, using confusion matrix and classification report for evaluation, and understanding precision and recall. it includes practical exercises and highlights a specific example with high accuracy score of the model.', 'chapters': [{'end': 621.789, 'start': 530.006, 'title': 'Knn classifier training and model accuracy', 'summary': 'Covers the training of a knn classifier, computing its accuracy, and the importance of finding the optimal value of k for model performance, with an emphasis on using grid search cv or k fold cross validation to determine the best k value for the use case.', 'duration': 91.783, 'highlights': ['The importance of finding the optimal value of K for model performance is emphasized, with suggestions to use grid search CV or K fold cross validation to determine the best K value for the use case.', 'The process of training a KNN classifier by calling the knn fit method on x train and y train is explained.', 'The method of computing the accuracy of the trained classifier by comparing predictions with the Y test data is highlighted, with an emphasis on the model achieving high accuracy.', 'The impact of changing the value of K on the score of the model is mentioned, indicating that different values of K result in different scores.']}, {'end': 800.877, 'start': 621.789, 'title': 'Confusion matrix and classification report', 'summary': 'Discusses the use of confusion matrix and classification report to evaluate the performance of a model, with a focus on a specific example where out of 30 samples, the model made only one wrong prediction, resulting in a high accuracy score.', 'duration': 179.088, 'highlights': ["The confusion matrix provides a visual representation of the model's correct and incorrect predictions for each class. The confusion matrix allows visualization of correct and incorrect predictions for each class, providing insights into the model's performance.", 'In the example, out of 30 samples, the model made only one wrong prediction, resulting in a high accuracy score. Out of the 30 samples, the model made only one incorrect prediction, leading to a high accuracy score and indicating the effectiveness of the model.', "The classification report from sklearn matrix library provides Precision, Recall, and F1-score for each class. The classification report offers detailed metrics such as Precision, Recall, and F1-score for each class, aiding in a comprehensive evaluation of the model's performance."]}, {'end': 941.24, 'start': 800.937, 'title': 'Understanding knn classifier basics', 'summary': 'Provides an introduction to knn classifier, emphasizing the importance of understanding precision and recall, followed by a practical exercise to classify digits using the classifier and find the optimal value of k.', 'duration': 140.303, 'highlights': ['The exercise of classifying digits using KNN classifier is emphasized as the most important part of the tutorial, stressing the importance of practice in learning coding and machine learning.', 'The importance of finding the optimal value of k for the KNN classifier is highlighted, with the recommendation for students to try on their own before checking the solution.', 'The simplicity and ease of understanding of KNN classifier is emphasized, with the example of explaining it to high school students and the simplicity of the code demonstrated.', 'The recommendation to watch a specific video on YouTube to understand the parameters of Precision and Recall is provided, offering a practical resource for learning.', 'The chapter concludes with an invitation for questions and comments, encouraging engagement and interaction with the content.']}], 'duration': 411.234, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/CQveSaMyEwM/pics/CQveSaMyEwM530006.jpg', 'highlights': ['The process of training a KNN classifier by calling the knn fit method on x train and y train is explained.', 'The method of computing the accuracy of the trained classifier by comparing predictions with the Y test data is highlighted, with an emphasis on the model achieving high accuracy.', "The confusion matrix provides a visual representation of the model's correct and incorrect predictions for each class, offering insights into the model's performance.", "The classification report from sklearn matrix library provides Precision, Recall, and F1-score for each class, aiding in a comprehensive evaluation of the model's performance.", 'The importance of finding the optimal value of K for model performance is emphasized, with suggestions to use grid search CV or K fold cross validation to determine the best K value for the use case.']}], 'highlights': ["The KNN algorithm's classification accuracy is influenced by the value of K, as demonstrated by the misclassification of a yellow data point as versicolor instead of virginica when K is set to 20, emphasizing the significance of choosing an appropriate K value to ensure accurate classification.", 'The method of computing the accuracy of the trained classifier by comparing predictions with the Y test data is highlighted, with an emphasis on the model achieving high accuracy.', "The confusion matrix provides a visual representation of the model's correct and incorrect predictions for each class, offering insights into the model's performance.", "The classification report from sklearn matrix library provides Precision, Recall, and F1-score for each class, aiding in a comprehensive evaluation of the model's performance.", 'The importance of finding the optimal value of K for model performance is emphasized, with suggestions to use grid search CV or K fold cross validation to determine the best K value for the use case.', 'Demonstrates k-nearest-neighbor classification for classifying iris flowers into three categories based on sepal width and height.', 'Covers theory, Python code implementation, model building, and exercise related to k-nearest-neighbor classification.', "The KNN algorithm's simplicity lies in its process of classifying a new data point based on the category of the most nearby K data points, as illustrated by the determination of the category of a new data point based on the highest count of nearby data points belonging to a specific category.", 'The KNN algorithm accommodates n number of features, demonstrated with the iris flower dataset.', 'Python is used for implementing the KNN algorithm, with reference to a YouTube video on support vector machine for additional context and code reference.', 'Loading the iris dataset from sklearn datasets and defining the feature names, including sepal width and length, petal width and length, is discussed in detail.', 'The chapter covers the classification of iris flowers into setosa, versicolor, and virginica based on four features, with 50 samples for each class.', 'The KNN classifier is created with K=3, using the default values for other parameters, and utilizing the Minkowski metric for computing Euclidean distance.', 'The dataset is divided into a training set with 120 samples and a test set with 30 samples to train and evaluate the KNN classifier.', 'The process of training a KNN classifier by calling the knn fit method on x train and y train is explained.']}