Evaluating Classification and Regression Systems

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3:45:19

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1.1 Overfitting Underfitting I - Synthetic Data-1 yphjgmjr.mp4

06:41

1.2 Overfitting Underfitting II - Varying Model Complexity-1 4jc84dfq.mp4

14:40

1.3 Errors and Costs-1 364pwj7e.mp4

04:49

1.4 Cross-Validation-1 wcmktsak.mp4

05:45

1.5 Leave-One-Out Cross-Validation-1 hou0uzn8.mp4

04:20

1.6 Stratification-1 7661gb5m.mp4

05:59

1.7 Repeated Train-Test Splits-1 vcl9pphi.mp4

07:05

1.8 Getting Graphical - Learning and Complexity Curves-1 064j1pbp.mp4

11:45

1.9 Graphical Cross-Validation-1 wb2bx7cm.mp4

04:28

2.1 Baseline Classifiers and Classification Metrics-1 6zplg1w6.mp4

08:28

2.2 The Confusion Matrix-1 kosvwu6a.mp4

08:19

2.3 Metrics from the Binary Confusion Matrix-1 46mxjpcf.mp4

18:17

2.4 Understanding the ROC Curve and AUC-1 p7isvg1c.mp4

12:59

2.5 Comparing Classifiers with ROC and PR Curves-1 pf3lihbt.mp4

08:04

3.1 Multi-class Metric Averages-1 yuamn4h2.mp4

13:00

3.2 Multi-class AUC - One-versus-Rest-1 ennowo9v.mp4

06:26

3.3 Multi-class AUC - The Hand and Till Method-1 6a7v3mxv.mp4

05:48

3.4 Cumulative Response and Lift Curves-1 1laascbx.mp4

05:49

3.5 Case Study - A Classifier Comparison-1 zp5u03mz.mp4

09:36

4.1 Baseline Regressors-1 d7296qus.mp4

06:18

4.2 Regression Metrics - Custom Metrics and RMSE-1 y1esj7mf.mp4

07:39

4.3 Understanding the Default Regression Metric R^2-1 ur2v7gpn.mp4

14:52

4.4 Errors and Residual Plots-1 6oyh6r9k.mp4

11:17

4.5 A Quick Pipeline and Standardization-1 owjtdyq9.mp4

10:11

4.6 Case Study - A Regressor Comparison-1 ptued3au.mp4

12:44

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Regression Analysis

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