Liquidity Regressors in Python – Felix

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Liquidity Regressors in Python

Practice splitting data into training and testing sets, constructing model pipelines, and performing hyperparameter tuning. As well as cross-validating alternative models to find the top performer

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30 Lessons (55m)

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1. Liquidity Regressor Learning Objectives

00:21
2. Case Study Overview

03:01
3. Importing Data Workout

01:09
4. Metadata

01:09
5. Metadata Workout

00:35
6. Splitting Your Data

01:57
7. Splitting Your Data Workout

02:04
8. Train Test Split

02:46
9. Unpacking Lists

01:51
10. Unpacking Lists Workout

01:24
11. Progress Checkpoint

00:13
12. Model Pipelines

04:38
13. Model Pipelines Workout

01:59
14. Progress Checkpoint 1

00:44
15. Hyperparameter Tuning

04:32
16. Hyperparameter Grid Workout 1

02:21
17. Hyperparameter Grid Workout 2

02:27
18. Aggregating Hyperparameter Grids

00:50
19. Progress Checkpoint 2

00:42
20. Cross Validation

02:06
21. Creating Untrained Models

02:13
22. Creating Untrained Models Workout

03:49
23. Training and Tuning Models

00:55
24. Training and Tuning Models Workout

02:29
25. Model Evaluation

02:02
26. Model Evaluation Workout

03:00
27. Progress Checkpoint 3

00:31
28. Visualizing Model Predictions

01:22
29. Visualizing Model Predictions Workout

01:04
30. Using Your Model

00:57

Prev: Regression Algorithms in Python Next: Classification Algorithms

Model Pipelines Workout

Notes
Questions

01:59

How to verify the proper definition of machine learning pipelines, including lasso, ridge, elastic net, random forest, and gradient booster.

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Glossary

elastic net gradient booster lasso Machine Learning Python random forest ridge

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