What are the methods for evaluating machine learning models?

Question

Accepted Answer

Methods for evaluating machine learning models include accuracy, precision, recall, F1 score, ROC curve, and confusion matrix.

Accuracy: measures the proportion of correct predictions out of the total predictions made by the model.
Precision: measures the proportion of true positive predictions out of all positive predictions made by the model.
Recall: measures the proportion of true positive predictions out of all actual positive instances.
F1 score: balances precision and recall, providing a single score that takes both into account.
ROC curve: plots the true positive rate against the false positive rate at various threshold settings.
Confusion matrix: a table that summarizes the performance of a classification model.

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