What are the best practices for evaluating machine learning models?
Some best practices include using a separate validation set, cross-validation, and performance metrics like accuracy and F1 score.
Why is cross-validation important?
Cross-validation helps ensure that your model generalizes well to unseen data by testing it on different subsets of the dataset.
What metrics should I use for evaluation?
It depends on your problem type. For classification, use accuracy, precision, recall, and F1 score. For regression, consider RMSE or R².
How can I avoid overfitting during evaluation?
To avoid overfitting, use techniques like regularization, pruning, and ensuring your training data is representative of the real-world scenario.
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