Pearson Evaluating Classification and Regression Systems Part 2 Sneak Peek
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3:45:19
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00001 1.1 Overfitting Underfitting I - AOSynthetic Data.mp4
06:41
00002 1.2 Overfitting Underfitting II - AOVarying Model Complexity.mp4
14:40
00003 1.3 Errors and Costs.mp4
04:49
00004 1.4 Cross-Validation.mp4
05:45
00005 1.5 Leave-One-Out Cross-Validation.mp4
04:20
00006 1.6 Stratification.mp4
05:59
00007 1.7 Repeated Train-Test Splits.mp4
07:05
00008 1.8 Getting Graphical - AOLearning and Complexity Curves.mp4
11:45
00009 1.9 Graphical Cross-Validation.mp4
04:28
00010 2.1 Baseline Classifiers and Classification Metrics.mp4
08:28
00011 2.2 The Confusion Matrix.mp4
08:19
00012 2.3 Metrics from the Binary Confusion Matrix.mp4
18:17
00013 2.4 Understanding the ROC Curve and AUC.mp4
12:59
00014 2.5 Comparing Classifiers with ROC and PR Curves.mp4
08:04
00015 3.1 Multi-class Metric Averages.mp4
13:00
00016 3.2 Multi-class AUC - One-versus-Rest.mp4
06:26
00017 3.3 Multi-class AUC - The Hand and Till Method.mp4
05:48
00018 3.4 Cumulative Response and Lift Curves.mp4
05:49
00019 3.5 Case Study - A Classifier Comparison.mp4
09:36
00020 4.1 Baseline Regressors.mp4
06:18
00021 4.2 Regression Metrics - Custom Metrics and RMSE.mp4
07:39
00022 4.3 Understanding the Default Regression Metric R 2.mp4
14:52
00023 4.4 Errors and Residual Plots.mp4
11:17
00024 4.5 A Quick Pipeline and Standardization.mp4
10:11
00025 4.6 Case Study - A Regressor Comparison.mp4
12:44
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- language english
- Training sessions 25
- duration 3:45:19
- Release Date 2023/11/06
