Skip to main content

A metric is a way to evaluate the performance of a machine learning model. Depending on the task, different metrics may be used.

A metric is a way to evaluate the performance of a machine learning model. Depending on the task, different metrics may be used. Some popular metrics used in often in classification tasks include accuracy, precision, recall, F1 Score, AUC. Metrics for regression tasks may be RMSE or MSE.

Some popular metrics are defined below:

$Accuracy = {Correct\ Predictions \over Total\ Predictions}$

$Precision = {True\ Positive \over {True\ Positive\ +\ False\ Positive}}$

$Recall = {True\ Positive \over {True\ Positive\ +\ False\ Negative}}$