Evaluating and improving machine learning models
Model evaluation is crucial for assessing the performance of machine learning models and ensuring they generalize well to new data. Key techniques include: train-test splits, cross-validation, hyperparameter tuning, and various evaluation metrics. Cross-validation provides a more robust estimate of model performance by using multiple train-test splits. Hyperparameter tuning (like GridSearchCV or RandomizedSearchCV) finds the best model parameters. Evaluation metrics depend on the problem type: accuracy, precision, recall, F1-score, ROC curves for classification; MSE, MAE, R² for regression. Understanding model evaluation helps prevent overfitting (model memorizing training data) and underfitting (model too simple). Proper evaluation ensures that models will perform well on unseen data, which is the ultimate goal of machine learning.