Model Evaluation and Refinement

 

This passage provides an overview of model evaluation techniques, focusing on the importance of assessing a model's performance on unseen data, and introduces the concept of cross-validation.

Training and Testing Data Split: It explains the concept of splitting the dataset into training and testing sets to evaluate the model's performance on unseen data. The train_test_split function from scikit-learn is mentioned as a tool for achieving this.

Generalization Error: Generalization error is introduced as a measure of how well the model performs on new, unseen data. It emphasizes the importance of using a substantial portion of data for training while also retaining a portion for testing to estimate the generalization error accurately.

Cross-Validation: The passage discusses cross-validation as a technique to evaluate a model's performance by splitting the dataset into multiple subsets or folds. It mentions cross_val_score as a method for performing cross-validation and obtaining evaluation metrics such as R^2. Additionally, cross_val_predict is introduced as a method to obtain predicted values for each data point during cross-validation, allowing for a more detailed analysis of model performance.

Overall, the passage emphasizes the importance of evaluating a model's performance on unseen data using techniques like train-test split and cross-validation to estimate its generalization error accurately. Additionally, it provides insights into how to obtain predicted values during cross-validation for a more comprehensive evaluation of the model.