How do I test machine learning models effectively?

How do I test machine learning models effectively?

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

What are some common performance metrics I should consider?

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

How can I improve the accuracy of my model?

You can improve accuracy by tuning hyperparameters, using more data, feature engineering, or trying different algorithms.

What is cross-validation and why is it important?

Cross-validation is a technique to assess how the results of a statistical analysis will generalize to an independent dataset. It's important for avoiding overfitting.