How do I effectively test my machine learning models?

How do I effectively test my machine learning models?

To effectively test your ML models, you should use techniques like cross-validation, holdout validation, and performance metrics.

What performance metrics should I focus on?

Key metrics include accuracy, precision, recall, F1 score, and AUC-ROC, depending on your specific use case.

Can you explain cross-validation?

Cross-validation involves splitting your dataset into training and testing sets multiple times to ensure your model's robustness.

How do I handle overfitting during testing?

To handle overfitting, you can use techniques like regularization, pruning, and ensuring you have enough training data.