Evaluating Machine Learning Models

Evaluating machine learning models is crucial to understand their performance and generalizability. Common evaluation metrics include:

Accuracy: The ratio of correctly predicted instances to total instances.
Precision: The ratio of true positives to the sum of true and false positives.
Recall: The ratio of true positives to the sum of true positives and false negatives.
F1 Score: The harmonic mean of precision and recall.

Cross-Validation

Cross-validation helps in assessing how the results of a statistical analysis will generalize to an independent dataset.