Regression evaluation metrics are used to measure how well a model predicts continuous values in Machine Learning. Common metrics include Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), which quantify prediction errors. The coefficient of determination (R² score) indicates how well the model explains the variance in the data. These metrics help in comparing models and selecting the most accurate one for real-world applications.
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