Whats the evaluation mertics for classification and regression model?bias and variance

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Accepted Answer

Evaluation metrics for classification and regression models are different. Bias and variance are important factors to consider.

Classification metrics include accuracy, precision, recall, F1 score, ROC curve, and AUC.
Regression metrics include mean squared error, mean absolute error, R-squared, and adjusted R-squared.
Bias refers to the difference between the predicted values and the actual values, while variance refers to the variability of the model's predictions.
High bias indicates underfitting, while high variance indicates overfitting.
The goal is to find a balance between bias and variance to achieve the best model performance.

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