title
Tutorial 34- Performance Metrics For Classification Problem In Machine Learning- Part1

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{'title': 'Tutorial 34- Performance Metrics For Classification Problem In Machine Learning- Part1', 'heatmap': [{'end': 887.537, 'start': 811.972, 'weight': 0.723}, {'end': 1310.496, 'start': 1277.235, 'weight': 1}], 'summary': 'Tutorial covers various metrics like confusion matrix, accuracy, recall, and precision used to evaluate classification problems, emphasizing the importance of selecting the right metrics for model evaluation and deployment. it also discusses threshold value selection, class imbalance, and the limitations of accuracy in imbalanced data, highlighting the need to focus on reducing type 1 and type 2 errors. the tutorial emphasizes the significance of reducing false positive and false negative values in predictive models, introduces the concept of f beta score, and discusses the impact of f beta value in selecting the right metrics for combining precision and recall, with examples of adjusting beta values based on the impact of false positives and false negatives.', 'chapters': [{'end': 114.735, 'segs': [{'end': 61.647, 'src': 'embed', 'start': 15.724, 'weight': 0, 'content': [{'end': 23.948, 'text': "and I've listed down all the important metrics that you can actually use for understanding whether your machine learning algorithm is predicting well or not.", 'start': 15.724, 'duration': 8.224}, {'end': 28.05, 'text': 'So some of the matrix over here are confusion matrix.', 'start': 24.908, 'duration': 3.142}, {'end': 30.292, 'text': 'then we will understand about accuracy.', 'start': 28.05, 'duration': 2.242}, {'end': 33.094, 'text': 'then we will understand about type 1 error, type 2 error.', 'start': 30.292, 'duration': 2.802}, {'end': 37.376, 'text': 'then we have concepts like recall, which is also called as true positive read.', 'start': 33.094, 'duration': 4.282}, {'end': 43.92, 'text': 'then we will discuss about precision, which is also called as positive prediction value.', 'start': 37.376, 'duration': 6.544}, {'end': 52.504, 'text': "then we will understand about F beta and in the next part, in the next video, we'll basically be understanding about cohen kappa, roc curve,", 'start': 43.92, 'duration': 8.584}, {'end': 55.845, 'text': 'auc score and something called as pr curve.', 'start': 52.504, 'duration': 3.341}, {'end': 61.647, 'text': "there are two more metrics again which i'll discuss in the part two, because i did not have space to write it down,", 'start': 55.845, 'duration': 5.802}], 'summary': 'Learn about important metrics for evaluating ml algorithms like confusion matrix, accuracy, errors, recall, precision, f beta, cohen kappa, roc curve, auc score, and pr curve.', 'duration': 45.923, 'max_score': 15.724, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww15724.jpg'}, {'end': 114.735, 'src': 'embed', 'start': 80.455, 'weight': 1, 'content': [{'end': 83.517, 'text': 'Okay, the reason why I am saying, even though you are a very,', 'start': 80.455, 'duration': 3.062}, {'end': 89.361, 'text': 'very good data scientist And you know how to actually use a machine learning algorithm with respect to your data,', 'start': 83.517, 'duration': 5.844}, {'end': 96.927, 'text': 'But if you are not using the correct kind of metrics to find out how good your model is, then it is completely a waste of time, You know,', 'start': 89.361, 'duration': 7.566}, {'end': 102.43, 'text': 'because if you have not selected the right metrics and then you have deployed your model to the production right?', 'start': 96.927, 'duration': 5.503}, {'end': 109.333, 'text': "you'll be able to see that because of the metrics, because of the wrong metrics that you have chosen, you have chosen,", 'start': 103.371, 'duration': 5.962}, {'end': 114.735, 'text': 'that will actually give you a very, very bad accuracy again when the model is actually deployed in the production.', 'start': 109.333, 'duration': 5.402}], 'summary': 'Selecting the right metrics is crucial for model accuracy in production.', 'duration': 34.28, 'max_score': 80.455, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww80455.jpg'}], 'start': 0.657, 'title': 'Metrics in classification problems', 'summary': 'Discusses various metrics like confusion matrix, accuracy, recall, and precision used to evaluate a classification problem, emphasizing the importance of selecting the right metrics for model evaluation and deployment.', 'chapters': [{'end': 114.735, 'start': 0.657, 'title': 'Metrics in classification problems', 'summary': 'Discusses various metrics used to evaluate a classification problem, including confusion matrix, accuracy, type 1 and type 2 errors, recall, precision, and future topics to be covered, emphasizing the importance of selecting the right metrics for model evaluation and deployment.', 'duration': 114.078, 'highlights': ['Understanding the importance of selecting the right metrics for model evaluation and deployment, as it can significantly impact the accuracy of the model when deployed in production.', 'Listing important metrics for evaluating machine learning algorithms in a classification problem, including confusion matrix, accuracy, type 1 and type 2 errors, recall, precision, and F beta.', 'Planning to cover additional metrics such as cohen kappa, roc curve, auc score, and pr curve in the next video, with further metrics to be discussed in subsequent parts.', 'Emphasizing the significance of correctly assessing model performance using appropriate metrics, as even skilled data scientists may face inaccuracies if incorrect metrics are chosen for evaluation.']}], 'duration': 114.078, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww657.jpg', 'highlights': ['Listing important metrics for evaluating machine learning algorithms in a classification problem, including confusion matrix, accuracy, type 1 and type 2 errors, recall, precision, and F beta.', 'Understanding the importance of selecting the right metrics for model evaluation and deployment, as it can significantly impact the accuracy of the model when deployed in production.', 'Emphasizing the significance of correctly assessing model performance using appropriate metrics, as even skilled data scientists may face inaccuracies if incorrect metrics are chosen for evaluation.', 'Planning to cover additional metrics such as cohen kappa, roc curve, auc score, and pr curve in the next video, with further metrics to be discussed in subsequent parts.']}, {'end': 729.181, 'segs': [{'end': 168.035, 'src': 'embed', 'start': 137.884, 'weight': 4, 'content': [{'end': 139.665, 'text': 'Classification problem statement.', 'start': 137.884, 'duration': 1.781}, {'end': 147.229, 'text': 'Now in classification problem statement there are two ways how you can solve the classification problem.', 'start': 140.226, 'duration': 7.003}, {'end': 150.551, 'text': 'One way is basically through class labels.', 'start': 147.83, 'duration': 2.721}, {'end': 153.593, 'text': 'Suppose you want to predict class labels.', 'start': 151.572, 'duration': 2.021}, {'end': 160.57, 'text': 'okay, the next way is through probabilities, probabilities.', 'start': 154.986, 'duration': 5.584}, {'end': 164.112, 'text': 'now, suppose i, if let me just consider a binary classification.', 'start': 160.57, 'duration': 3.542}, {'end': 168.035, 'text': 'in a binary classification i know there will be two different classes, a or b.', 'start': 164.112, 'duration': 3.923}], 'summary': 'Classify data using labels or probabilities, e.g., binary a or b classes.', 'duration': 30.151, 'max_score': 137.884, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww137884.jpg'}, {'end': 236.558, 'src': 'embed', 'start': 208.758, 'weight': 0, 'content': [{'end': 211.739, 'text': 'In some of the healthcare sector this threshold value may decrease.', 'start': 208.758, 'duration': 2.981}, {'end': 219.404, 'text': 'You will be saying that Suppose if a person is having cancer or not, at that time this threshold value should be chosen in a proper way.', 'start': 212.139, 'duration': 7.265}, {'end': 224.208, 'text': 'If it is not chosen in a proper way, the person who is having cancer will be missed out.', 'start': 219.865, 'duration': 4.343}, {'end': 230.073, 'text': 'So in probabilities we will be discussing, and in probabilities, what all we have?', 'start': 225.289, 'duration': 4.784}, {'end': 236.558, 'text': 'We have basically ROC curve, AUC curve, AUC score and PR curve, which we will be discussing in the part 2..', 'start': 230.113, 'duration': 6.445}], 'summary': 'In healthcare, choosing the proper threshold value is crucial to avoid missing out on diagnosing cancer. probabilities will be discussed using roc curve, auc curve, auc score, and pr curve in the next part.', 'duration': 27.8, 'max_score': 208.758, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww208758.jpg'}, {'end': 450.665, 'src': 'embed', 'start': 419.526, 'weight': 3, 'content': [{'end': 421.807, 'text': 'we have an imbalanced data set.', 'start': 419.526, 'duration': 2.281}, {'end': 424.869, 'text': 'at that time, we do not consider accuracy.', 'start': 421.807, 'duration': 3.062}, {'end': 429.332, 'text': 'instead, we consider something called as recall, precision and F beta.', 'start': 424.869, 'duration': 4.463}, {'end': 435.255, 'text': "I'll explain you about what exactly is F beta score, which is also called as F1 score, if you have heard of most of it.", 'start': 429.332, 'duration': 5.923}, {'end': 440.558, 'text': "but this F1 score is derived by this beta value that we'll be discussing about now.", 'start': 435.255, 'duration': 5.303}, {'end': 443.76, 'text': 'just consider, guys, initially Let us take that.', 'start': 440.558, 'duration': 3.202}, {'end': 450.665, 'text': "suppose my data set is balanced, okay, at that time I'll try to explain you accuracy and then we will then understand.", 'start': 443.76, 'duration': 6.905}], 'summary': 'Imbalanced data set analyzed using recall, precision, f1 score, and beta value.', 'duration': 31.139, 'max_score': 419.526, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww419526.jpg'}, {'end': 491.584, 'src': 'embed', 'start': 461.453, 'weight': 2, 'content': [{'end': 463.675, 'text': "see this particular explanation, what I've given.", 'start': 461.453, 'duration': 2.222}, {'end': 470.664, 'text': 'okay, Now, first of all, if we have a binary classification problem, guys, we need to understand what exactly is a confusion matrix?', 'start': 463.675, 'duration': 6.989}, {'end': 478.71, 'text': 'Now understand one thing, guys confusion matrix is nothing, but it is a 2x2 matrix in case of binary classification problem,', 'start': 471.225, 'duration': 7.485}, {'end': 482.613, 'text': 'where the top values are actually the actual values.', 'start': 478.71, 'duration': 3.903}, {'end': 491.584, 'text': 'The top values are the actual values over here actual values like 0 or 1, 1 or 0 suppose I consider this as 1 and 0.', 'start': 484.395, 'duration': 7.189}], 'summary': 'Binary classification uses a 2x2 confusion matrix to represent actual values.', 'duration': 30.131, 'max_score': 461.453, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww461453.jpg'}, {'end': 668.513, 'src': 'embed', 'start': 640.214, 'weight': 1, 'content': [{'end': 648.219, 'text': 'usually my model does not gets biased based on the different types of categories that we have in this binary classification problem.', 'start': 640.214, 'duration': 8.005}, {'end': 652.082, 'text': 'But now, what if my data set is not balanced??', 'start': 648.579, 'duration': 3.503}, {'end': 654.443, 'text': 'What if my data set is not balanced??', 'start': 652.882, 'duration': 1.561}, {'end': 656.285, 'text': 'Let me give you a very good example.', 'start': 654.463, 'duration': 1.822}, {'end': 658.987, 'text': 'Suppose one category, one category.', 'start': 656.285, 'duration': 2.702}, {'end': 661.368, 'text': 'I have some hundred, nine hundred values.', 'start': 659.127, 'duration': 2.241}, {'end': 668.513, 'text': 'suppose, out of the thousand records I have nine hundred one category and hundred as my another category.', 'start': 661.368, 'duration': 7.145}], 'summary': 'Model may get biased due to data imbalance in binary classification with 900:100 ratio.', 'duration': 28.299, 'max_score': 640.214, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww640214.jpg'}], 'start': 114.735, 'title': 'Understanding classification metrics', 'summary': 'Explains classification problem statements, threshold value selection, class imbalance, confusion matrix usage, and accuracy computation, with a focus on healthcare sector implications and the need for different metrics in imbalanced data sets.', 'chapters': [{'end': 230.073, 'start': 114.735, 'title': 'Understanding classification metrics', 'summary': 'Explains the concept of classification problem statements, the two ways to solve them, and the importance of selecting the right threshold value for probabilities, with a focus on healthcare sector implications.', 'duration': 115.338, 'highlights': ['The chapter discusses the two ways to solve a classification problem statement: through class labels and through probabilities, with a focus on binary classification and the selection of the threshold value (P value) for probabilities.', 'In the healthcare sector, the proper selection of the threshold value for probabilities is crucial, especially in scenarios like identifying cancer, to prevent missing out on detecting the condition.']}, {'end': 729.181, 'start': 230.113, 'title': 'Understanding classification metrics', 'summary': 'Explains the concepts of class imbalance in binary classification problems, the use of confusion matrix, and the computation of accuracy in balanced data sets, emphasizing the need for different metrics in imbalanced data sets.', 'duration': 499.068, 'highlights': ['The chapter explains the concepts of class imbalance in binary classification problems The speaker discusses the impact of class imbalance on binary classification problems, highlighting scenarios with unbalanced data sets and the implications for machine learning algorithms.', 'The use of confusion matrix and the computation of accuracy in balanced data sets The concept of confusion matrix in binary classification problems is detailed, emphasizing the calculation of accuracy in balanced data sets through TP, TN, FP, and FN, highlighting the method for accuracy computation.', 'The need for different metrics in imbalanced data sets The speaker explains the need to consider recall, precision, and F beta score instead of accuracy in imbalanced data sets, emphasizing the impact of imbalanced data on type 1 and type 2 errors in classification problems.']}], 'duration': 614.446, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww114735.jpg', 'highlights': ['The proper selection of the threshold value for probabilities is crucial in healthcare, especially in scenarios like identifying cancer.', 'The impact of class imbalance on binary classification problems is discussed, highlighting scenarios with unbalanced data sets and the implications for machine learning algorithms.', 'The concept of confusion matrix in binary classification problems is detailed, emphasizing the calculation of accuracy in balanced data sets through TP, TN, FP, and FN.', 'The need to consider recall, precision, and F beta score instead of accuracy in imbalanced data sets is explained, emphasizing the impact of imbalanced data on type 1 and type 2 errors in classification problems.', 'The chapter discusses the two ways to solve a classification problem statement: through class labels and through probabilities, with a focus on binary classification and the selection of the threshold value (P value) for probabilities.']}, {'end': 932.612, 'segs': [{'end': 887.537, 'src': 'heatmap', 'start': 748.835, 'weight': 0, 'content': [{'end': 755.599, 'text': 'if we have an imbalanced data set, we cannot just use accuracy, because it will give you a very, very bad meaning about that particular model.', 'start': 748.835, 'duration': 6.764}, {'end': 759.321, 'text': 'You are just blindly saying that it belongs to just one category.', 'start': 756.519, 'duration': 2.802}, {'end': 769.446, 'text': 'So if you have an imbalanced data set you basically go with something called as recall, precision and something called as F beta score.', 'start': 759.941, 'duration': 9.505}, {'end': 773.669, 'text': 'Now let us go ahead and try to understand about recall and precision.', 'start': 770.107, 'duration': 3.562}, {'end': 779.045, 'text': 'Okay guys now let us go ahead and understand what exactly is recall and precision.', 'start': 774.642, 'duration': 4.403}, {'end': 781.106, 'text': 'Now guys here is my confusion matrix.', 'start': 779.445, 'duration': 1.661}, {'end': 784.529, 'text': 'Here you have true positive, false positive, false negative and true negative.', 'start': 781.547, 'duration': 2.982}, {'end': 787.811, 'text': 'Understand why do we use this for an imbalanced data set.', 'start': 784.949, 'duration': 2.862}, {'end': 795.977, 'text': 'Now understand one thing guys any kind of data set that you have you should always try to reduce your type 1 error and type 2 error.', 'start': 788.311, 'duration': 7.666}, {'end': 798.019, 'text': 'You should always try to reduce this.', 'start': 796.677, 'duration': 1.342}, {'end': 804.004, 'text': 'Now specifically when your data set is imbalanced, we should either focus on recall and precision.', 'start': 798.559, 'duration': 5.445}, {'end': 805.606, 'text': 'Now, what does recall over here?', 'start': 804.285, 'duration': 1.321}, {'end': 807.067, 'text': 'formula says? Recall.', 'start': 805.606, 'duration': 1.461}, {'end': 810.31, 'text': 'basically says that TP.', 'start': 807.067, 'duration': 3.243}, {'end': 811.671, 'text': 'so these are my actual values, right?', 'start': 810.31, 'duration': 1.361}, {'end': 814.354, 'text': 'These are my actual, these are my predicted values.', 'start': 811.972, 'duration': 2.382}, {'end': 819.239, 'text': 'So this basically says that TP divided by TP plus FN.', 'start': 815.135, 'duration': 4.104}, {'end': 823.335, 'text': 'okay, TP divided by TP plus FM.', 'start': 820.413, 'duration': 2.922}, {'end': 824.116, 'text': 'now, what does this say?', 'start': 823.335, 'duration': 0.781}, {'end': 832.522, 'text': 'is that out of the total positive actual values, how many values did we correctly predicted positively?', 'start': 824.116, 'duration': 8.406}, {'end': 834.703, 'text': 'okay, this is what this recall basically says.', 'start': 832.522, 'duration': 2.181}, {'end': 844.13, 'text': 'again, I am repeating it, guys, out of the total actual positive values, one is positive, right, I can say true or positive, anything of all this.', 'start': 834.703, 'duration': 9.427}, {'end': 847.652, 'text': 'how many positive did we predict correctly?', 'start': 844.13, 'duration': 3.522}, {'end': 849.573, 'text': 'that is what this recall basically says.', 'start': 847.652, 'duration': 1.921}, {'end': 853.476, 'text': 'recall is also given by something called as true positive rate.', 'start': 849.573, 'duration': 3.903}, {'end': 858.139, 'text': 'it is also mentioned by true positive rate, or it is also mentioned by sensitivity.', 'start': 853.476, 'duration': 4.663}, {'end': 860.66, 'text': 'okay, it is also mentioned by sensitivity now.', 'start': 858.139, 'duration': 2.521}, {'end': 865.884, 'text': 'similarly, if I consider about precision, it basically says TP divided by TP plus FP.', 'start': 860.66, 'duration': 5.224}, {'end': 869.626, 'text': 'okay, now, what does this basically say?', 'start': 866.704, 'duration': 2.922}, {'end': 877.05, 'text': 'out of the total predicted positive result, how many results were actual positive?', 'start': 869.626, 'duration': 7.424}, {'end': 879.772, 'text': 'okay, here we are actually focusing on a false positive.', 'start': 877.05, 'duration': 2.722}, {'end': 883.874, 'text': 'here in the recall, we are actually focusing on false negative, right.', 'start': 879.772, 'duration': 4.102}, {'end': 887.537, 'text': "so again, i'm repeating it what does precision basically say?", 'start': 883.874, 'duration': 3.663}], 'summary': 'Imbalanced data requires focus on recall, precision, and reducing type 1 and type 2 errors. recall measures the correct prediction of positive values, while precision measures the actual positive results among predicted positives.', 'duration': 85.868, 'max_score': 748.835, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww748835.jpg'}], 'start': 729.941, 'title': 'Imbalanced data and metrics', 'summary': 'Discusses the limitations of accuracy in imbalanced data and emphasizes the importance of recall and precision, highlighting the need to focus on reducing type 1 and type 2 errors. it also explains the concepts of recall and precision in evaluating models, using a spam detection use case to illustrate their significance.', 'chapters': [{'end': 811.671, 'start': 729.941, 'title': 'Imbalanced data: accuracy vs recall and precision', 'summary': 'Explains the limitations of using accuracy in an imbalanced data set and emphasizes the importance of recall and precision, highlighting the need to focus on reducing type 1 and type 2 errors.', 'duration': 81.73, 'highlights': ['Imbalanced data sets require evaluation metrics like recall, precision, and F beta score, as accuracy may give misleading results. In an imbalanced data set, accuracy can give misleading results and it is important to use evaluation metrics like recall, precision, and F beta score instead.', 'Importance of reducing type 1 and type 2 errors in any data set, especially when dealing with imbalanced data. It is crucial to focus on reducing type 1 and type 2 errors in any data set, particularly in the context of imbalanced data.', 'Explanation of recall and precision in the context of a confusion matrix, emphasizing the significance of these metrics in imbalanced data sets. The chapter provides an explanation of recall and precision using a confusion matrix and highlights their significance in evaluating imbalanced data sets.']}, {'end': 932.612, 'start': 811.972, 'title': 'Understanding recall and precision metrics', 'summary': 'Explains the concepts of recall and precision in the context of evaluating models, with recall measuring the proportion of actual positives correctly identified and precision measuring the proportion of predicted positives that were actual positives, as illustrated using a spam detection use case.', 'duration': 120.64, 'highlights': ["Recall measures the proportion of actual positives correctly identified, given by TP / (TP + FN). The recall metric calculates the proportion of actual positive values that were correctly predicted as positive, providing a clear understanding of the model's ability to capture all positive instances. In the context of spam detection, this metric would be crucial in ensuring that a high percentage of actual spam emails are correctly classified.", 'Precision measures the proportion of predicted positives that were actual positives, given by TP / (TP + FP). Precision assesses the proportion of predicted positive results that were actually positive, emphasizing the focus on minimizing false positives. In the context of spam detection, precision would be essential in ensuring that the emails classified as spam are indeed spam, reducing the occurrence of false positives.']}], 'duration': 202.671, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww729941.jpg', 'highlights': ['Imbalanced data sets require evaluation metrics like recall, precision, and F beta score, as accuracy may give misleading results.', 'Importance of reducing type 1 and type 2 errors in any data set, especially when dealing with imbalanced data.', 'Explanation of recall and precision in the context of a confusion matrix, emphasizing the significance of these metrics in imbalanced data sets.', 'Recall measures the proportion of actual positives correctly identified, given by TP / (TP + FN).', 'Precision measures the proportion of predicted positives that were actual positives, given by TP / (TP + FP).']}, {'end': 1446.296, 'segs': [{'end': 996.44, 'src': 'embed', 'start': 969.227, 'weight': 1, 'content': [{'end': 975.571, 'text': 'if it is not a spam and if it is specified or predicted as a spam, then the customer is going to miss that particular mail,', 'start': 969.227, 'duration': 6.344}, {'end': 978.012, 'text': 'which may be a very important mail itself.', 'start': 975.571, 'duration': 2.441}, {'end': 985.396, 'text': 'So because of that, we should always try to reduce this false positive value in the case of this kind of use case, that is spam detection.', 'start': 978.773, 'duration': 6.623}, {'end': 989.918, 'text': 'But what about recall? Suppose I say that whether the person is having cancer or not.', 'start': 985.876, 'duration': 4.042}, {'end': 992.716, 'text': 'cancer or not?', 'start': 990.834, 'duration': 1.882}, {'end': 996.44, 'text': 'okay, so my one value basically specifies that he is having cancer.', 'start': 992.716, 'duration': 3.724}], 'summary': 'Reducing false positives in spam detection is crucial for not missing important emails; achieving high recall is crucial for detecting serious conditions like cancer.', 'duration': 27.213, 'max_score': 969.227, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww969227.jpg'}, {'end': 1097.975, 'src': 'embed', 'start': 1047.94, 'weight': 0, 'content': [{'end': 1056.466, 'text': 'now, in short, guys, whenever your false positive is much more important, whenever your false positive is much more important,', 'start': 1047.94, 'duration': 8.526}, {'end': 1060.208, 'text': 'go and blindly use precision.', 'start': 1056.466, 'duration': 3.742}, {'end': 1069.075, 'text': 'whenever, with respect to your problem statement, if your recall, if your false negative is important, at that time you go and use recall.', 'start': 1060.208, 'duration': 8.867}, {'end': 1074.427, 'text': 'I gave you an example, guys cancer, whether a person is having cancer or not.', 'start': 1070.086, 'duration': 4.341}, {'end': 1078.629, 'text': 'Some more examples, whether tomorrow the stock market is going to crash or not.', 'start': 1074.507, 'duration': 4.122}, {'end': 1082.19, 'text': 'Some example of precision, spam detection.', 'start': 1080.149, 'duration': 2.041}, {'end': 1085.211, 'text': 'Consider this particular example, try to think.', 'start': 1083.33, 'duration': 1.881}, {'end': 1089.252, 'text': 'Always our aim should be to reduce false positive and false negative.', 'start': 1085.951, 'duration': 3.301}, {'end': 1094.874, 'text': 'But whether false positive is playing a greater impact or role in that specific model.', 'start': 1089.592, 'duration': 5.282}, {'end': 1097.975, 'text': 'If it is playing, go and use precision, focus on precision.', 'start': 1095.114, 'duration': 2.861}], 'summary': 'Use precision when false positive is important, recall when false negative is important.', 'duration': 50.035, 'max_score': 1047.94, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww1047940.jpg'}, {'end': 1142.588, 'src': 'embed', 'start': 1120.197, 'weight': 3, 'content': [{'end': 1129.334, 'text': 'at that time we have to consider both recall and precision, And if you want to consider both recall and precision, we basically use F beta score.', 'start': 1120.197, 'duration': 9.137}, {'end': 1136.043, 'text': 'F beta score and sometimes in some of the problem statements, guys, even though recall play a major role,', 'start': 1131.04, 'duration': 5.003}, {'end': 1141.007, 'text': 'that is like false negative play a major role or a false positive play a major role.', 'start': 1136.043, 'duration': 4.964}, {'end': 1142.588, 'text': 'you know some of the problem statement.', 'start': 1141.007, 'duration': 1.581}], 'summary': 'F beta score balances recall and precision in problem statements.', 'duration': 22.391, 'max_score': 1120.197, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww1120197.jpg'}, {'end': 1310.496, 'src': 'heatmap', 'start': 1277.235, 'weight': 1, 'content': [{'end': 1279.097, 'text': 'both are equally important.', 'start': 1277.235, 'duration': 1.862}, {'end': 1281.719, 'text': 'Both are having a greater impact.', 'start': 1279.958, 'duration': 1.761}, {'end': 1286.337, 'text': 'At that time you go and select beta is equal to 1.', 'start': 1282.12, 'duration': 4.217}, {'end': 1294.223, 'text': 'Now in some of the scenarios suppose your false positive is having more impact than the false negative that is then the type 2 error.', 'start': 1286.337, 'duration': 7.886}, {'end': 1296.284, 'text': 'False positive is the type 1 error.', 'start': 1294.883, 'duration': 1.401}, {'end': 1302.068, 'text': 'At that time you reduce your beta value to 0.5 between 0 to 1.', 'start': 1296.724, 'duration': 5.344}, {'end': 1304.329, 'text': 'Usually people select it as 0.5.', 'start': 1302.068, 'duration': 2.261}, {'end': 1310.496, 'text': 'At that time, this beta value will get converted to 0.5, so it will be 1 plus 0.25.', 'start': 1304.329, 'duration': 6.167}], 'summary': 'Balancing false positives and false negatives with beta value, usually set at 0.5.', 'duration': 33.261, 'max_score': 1277.235, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww1277235.jpg'}, {'end': 1421.719, 'src': 'embed', 'start': 1390.242, 'weight': 4, 'content': [{'end': 1393.543, 'text': 'okay, considering where your false negative is having a greater impact.', 'start': 1390.242, 'duration': 3.301}, {'end': 1399.365, 'text': 'when your false positive is having a greater impact, you basically select a value somewhere between 0 to 1,', 'start': 1393.543, 'duration': 5.822}, {'end': 1411.03, 'text': 'and that is why we use specifically use f beta where you want to combine both precision and recall and try to showcase a particular problem statement and try to select the right kind of metrics.', 'start': 1399.365, 'duration': 11.665}, {'end': 1413.431, 'text': 'this, each and every parameter, is very, very important, guys.', 'start': 1411.03, 'duration': 2.401}, {'end': 1416.653, 'text': 'So I hope you understood this particular video guys.', 'start': 1414.591, 'duration': 2.062}, {'end': 1421.719, 'text': 'In the part 2 we will be discussing about Cohen Kappa, ROC curve, AUC score, PR curve,', 'start': 1416.954, 'duration': 4.765}], 'summary': 'Select a value between 0 to 1 to balance precision and recall, with focus on specific metrics like cohen kappa, roc curve, auc score, and pr curve.', 'duration': 31.477, 'max_score': 1390.242, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww1390242.jpg'}], 'start': 932.612, 'title': 'Importance of reducing false positives and negatives in predictive models', 'summary': 'Emphasizes the significance of reducing false positive and false negative values in predictive models, such as spam detection and cancer diagnosis, and highlights the potential consequences of misclassifications on customer experience and patient health. it also explains the importance of precision and recall, introduces the concept of f beta score, and discusses the impact of f beta value in selecting the right metrics for combining precision and recall, with examples of adjusting beta values based on the impact of false positives and false negatives.', 'chapters': [{'end': 1047.94, 'start': 932.612, 'title': 'False positive and false negative in predictive models', 'summary': 'Discusses the importance of reducing false positive and false negative values in predictive models such as spam detection and cancer diagnosis, emphasizing the potential consequences of misclassifications on customer experience and patient health.', 'duration': 115.328, 'highlights': ['In spam mail detection, reducing false positive values is crucial to ensure that important non-spam emails are not mistakenly classified as spam, impacting customer experience.', "When diagnosing diseases like cancer, minimizing false negative values is essential to prevent misclassifying individuals with the disease as healthy, potentially leading to disastrous consequences for the patients' health.", 'Misclassifying non-spam emails as spam (false positive) can lead to customers missing important emails, highlighting the significance of reducing false positive values in spam detection.', 'Misclassifying individuals without cancer as having the disease (false negative) can lead to delayed diagnosis and treatment, underscoring the importance of minimizing false negative values in disease diagnosis.']}, {'end': 1273.11, 'start': 1047.94, 'title': 'Understanding f beta score', 'summary': 'Explains the importance of precision and recall, introduces the concept of f beta score, and provides a detailed formula and explanation for selecting the beta value in the context of false positive and false negative in a problem statement.', 'duration': 225.17, 'highlights': ['The chapter emphasizes the importance of precision and recall in different problem statements, such as cancer detection and stock market prediction. The chapter illustrates the significance of precision and recall in various scenarios, such as cancer detection and stock market prediction.', 'It introduces the concept of F beta score as a metric to consider both recall and precision, especially in cases where false positive and false negative are both crucial in an imbalanced dataset. The concept of F beta score is introduced as a metric to consider both recall and precision, particularly in cases where false positive and false negative are both crucial in an imbalanced dataset.', 'The chapter explains the formula for F beta score and the selection of beta value, where different beta values (such as 0.5 and 2) are used to calculate F 0.5 score and F 2 score. The chapter provides a detailed explanation of the formula for F beta score and the selection of beta value, with the use of different beta values (e.g., 0.5 and 2) to calculate F 0.5 score and F 2 score.']}, {'end': 1446.296, 'start': 1273.11, 'title': 'Understanding f beta value and its impact', 'summary': 'Discusses the importance of f beta value in selecting the right metrics for combining precision and recall, with examples of adjusting beta values based on the impact of false positives and false negatives.', 'duration': 173.186, 'highlights': ['The importance of F beta value in combining precision and recall for selecting the right metrics is emphasized, with examples of adjusting beta values based on the impact of false positives and false negatives.', 'The impact of false positives and false negatives is illustrated, with guidance on adjusting beta values to account for their importance in the F beta score calculation.', 'The upcoming topics of discussion include Cohen Kappa, ROC curve, AUC score, PR curve, and practical problem statement implementation in part 2 and part 3 of the series.']}], 'duration': 513.684, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/aWAnNHXIKww/pics/aWAnNHXIKww932612.jpg', 'highlights': ["Minimizing false negative values is essential in disease diagnosis to prevent disastrous consequences for patients' health.", 'Reducing false positive values in spam detection is crucial to ensure important non-spam emails are not mistakenly classified as spam, impacting customer experience.', 'The chapter emphasizes the importance of precision and recall in different problem statements, such as cancer detection and stock market prediction.', 'The concept of F beta score is introduced as a metric to consider both recall and precision, particularly in cases where false positive and false negative are both crucial in an imbalanced dataset.', 'The importance of F beta value in combining precision and recall for selecting the right metrics is emphasized, with examples of adjusting beta values based on the impact of false positives and false negatives.']}], 'highlights': ['Listing important metrics for evaluating machine learning algorithms in a classification problem, including confusion matrix, accuracy, type 1 and type 2 errors, recall, precision, and F beta.', 'The concept of F beta score is introduced as a metric to consider both recall and precision, particularly in cases where false positive and false negative are both crucial in an imbalanced dataset.', 'The importance of F beta value in combining precision and recall for selecting the right metrics is emphasized, with examples of adjusting beta values based on the impact of false positives and false negatives.', 'The need to consider recall, precision, and F beta score instead of accuracy in imbalanced data sets is explained, emphasizing the impact of imbalanced data on type 1 and type 2 errors in classification problems.', 'Importance of reducing type 1 and type 2 errors in any data set, especially when dealing with imbalanced data.', 'Understanding the importance of selecting the right metrics for model evaluation and deployment, as it can significantly impact the accuracy of the model when deployed in production.', 'The chapter emphasizes the importance of precision and recall in different problem statements, such as cancer detection and stock market prediction.', 'The proper selection of the threshold value for probabilities is crucial in healthcare, especially in scenarios like identifying cancer.', 'The impact of class imbalance on binary classification problems is discussed, highlighting scenarios with unbalanced data sets and the implications for machine learning algorithms.', 'The concept of confusion matrix in binary classification problems is detailed, emphasizing the calculation of accuracy in balanced data sets through TP, TN, FP, and FN.']}