Model Evaluation & Optimization

Free

Beginner

10h

12 lessons

Develop skills to assess model performance using cross-validation, select appropriate metrics, tune hyperparameters, and prevent overfitting to ensure models generalize well.

Model Evaluation

Cross-Validation

Hyperparameter Tuning

Overfitting

Performance Metrics

Sign In Required

Create an account or sign in to access the lesson content

Sign Up Sign In

Lessons

1

Data Splitting: Train, Validation, & Test Sets

60 min

2

Cross-Validation Strategies: K-Fold & Stratified

60 min

3

Regression Performance: RMSE, MAE, & R-squared

60 min

Back to Path

Model Evaluation & Optimization

Free

Beginner

10h

12 lessons

Develop skills to assess model performance using cross-validation, select appropriate metrics, tune hyperparameters, and prevent overfitting to ensure models generalize well.

Model Evaluation

Cross-Validation

Hyperparameter Tuning

Overfitting

Performance Metrics

Sign In Required

Create an account or sign in to access the lesson content

Sign Up Sign In

Lessons

1

Data Splitting: Train, Validation, & Test Sets

60 min

2

Cross-Validation Strategies: K-Fold & Stratified

60 min

3

Regression Performance: RMSE, MAE, & R-squared

60 min

4

Classification Performance: Precision, Recall, & F1

60 min

5

Quiz #1

6

Hands-On #1

60 min

7

Bias-Variance Tradeoff: Diagnosing Model Fit

60 min

8

Hyperparameter Optimization: Grid & Random Search

60 min

9

Regularization Techniques: L1, L2, & Elastic Net

60 min

10

Final Model Selection: Best Practices

60 min

11

Hands-On #2

60 min

12

Model Evaluation & Optimization

Lessons

Data Splitting: Train, Validation, & Test Sets

Cross-Validation Strategies: K-Fold & Stratified

Regression Performance: RMSE, MAE, & R-squared

Model Evaluation & Optimization

Lessons

Data Splitting: Train, Validation, & Test Sets

Cross-Validation Strategies: K-Fold & Stratified

Regression Performance: RMSE, MAE, & R-squared

Classification Performance: Precision, Recall, & F1

Quiz #1

Hands-On #1

Bias-Variance Tradeoff: Diagnosing Model Fit

Hyperparameter Optimization: Grid & Random Search

Regularization Techniques: L1, L2, & Elastic Net

Final Model Selection: Best Practices

Hands-On #2

Quiz #2