Investor Classifiers in Python – Part 2 – Felix

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Investor Classifiers in Python - Part 2

Use stratified random sampling to select proportionate samples from categorical data. Understand the confusion matrix. Build and finalize a machine learning classifier from start to finish.

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24 Lessons (25m)

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1. Investor Classifiers Part 2 Learning Objectives

00:25
2. Import Packages and Data

00:38
3. .drop Workout

00:48
4. Dummy Variables

00:54
5. Dummy Variables Workout

00:36
6. Remove Redundant Target

00:45
7. Splitting Data

02:50
8. Splitting Data Workout

00:51
9. Model Pipelines

00:38
10. Model Pipelines Workout

01:39
11. Validating Pipelines

00:17
12. Hyperparameter Tuning

01:13
13. Hyperparameters Workout

00:32
14. Validating Hyperparameter Grids

00:13
15. Cross Validation

00:57
16. Cross-Validation Workout

01:13
17. Fitting Untrained Models

00:28
18. Fitting Untrained Models Workout

01:13
19. AUROC

01:27
20. Confusion Matrix

02:52
What the area under the ROC curve (AUROC) is, and its importance in evaluating the performance of a model.
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21. Perfect AUROC

01:32
22. Calculating AUROC

00:10
23. Calculating AUROC Workout

01:33
24. Investor Classifiers Part 2 Review

00:19

Prev: Investor Classifiers in Python - Part 1

Hyperparameter Tuning

Notes
Questions

01:13

Understand hyperparameter tuning for classification algorithms, including logistic regression, random forest, and gradient boosting classifiers, focusing on the creation of hyperparameter grids for cross-validation.

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Glossary

cross-validation gradient boosting classifiers hyperparameter grids logistic regression random forest

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