Glossary term

Mean squared error

A common measure of prediction error that averages the squared differences from the true values.

How it works

MSE is the average of the squared differences between predictions and true values. Squaring makes all errors positive and punishes large errors more than small ones. Its square root, RMSE, is in the same units as the data.

Regression: errors are the gaps from the line

In code

MSE

import numpy as np

mse = np.mean((y_true - y_pred) ** 2)

Why it matters

It is the standard loss and metric for predicting numbers.

Related terms

Loss function Regression Accuracy