Regression Metrics
After training a regression model, you need to measure how well it predicts. Common metrics quantify the difference between predicted and actual values.
Mean Absolute Error (MAE)
Average of absolute differences. Easy to interpret – same units as target.
from sklearn.metrics import mean_absolute_error
mae = mean_absolute_error(y_true, y_pred)Mean Squared Error (MSE)
Average of squared differences. Penalizes large errors more heavily.
from sklearn.metrics import mean_squared_error
mse = mean_squared_error(y_true, y_pred)Root Mean Squared Error (RMSE)
Square root of MSE. Same units as target, more interpretable than MSE.
rmse = mean_squared_error(y_true, y_pred, squared=False)R‑squared (R²)
Proportion of variance explained by the model. Ranges from -∞ to 1 (1 is perfect).
from sklearn.metrics import r2_score
r2 = r2_score(y_true, y_pred)Which Metric to Use?
- MAE: when outliers are not important.
- MSE/RMSE: when large errors are particularly bad.
- R²: to compare models or explain variance.
Two Minute Drill
- MAE = average absolute error.
- MSE = average squared error (penalizes large errors).
- RMSE = square root of MSE (same units as target).
- R² = proportion of variance explained (1 is perfect).
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