🤖 Machine Learning
Model Evaluation
Comprehensive guide to statistical model evaluation, covering confusion matrices, ROC curves, and cross-validation strategies.
● Intermediate
📖 Based on: Hands-On Machine Learning — Aurélien Géron
📋 Model Selection & Metrics
1 · Beyond Accuracy
Accuracy is often misleading in imbalanced datasets. We must use Precision, Recall, and the F1-Score to get a true picture of model performance.
4 · K-Fold Cross-Validation
Generalization Error
Cross-validation allows us to estimate the model's performance on unseen data. By splitting the training set into K folds and rotating the validation set, we ensure that every data point is used for both training and validation.