📈 03 — Regression Evaluation

Use regression metrics based on error cost.

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Core Metrics

from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score

mae = mean_absolute_error(y_test, y_pred)
rmse = mean_squared_error(y_test, y_pred, squared=False)
r2 = r2_score(y_test, y_pred)

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Error Analysis

errors = y_test - y_pred

Check:

• largest errors
• error by segment
• residual plot
• underprediction vs overprediction

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Reporting Template

Model: Random Forest Regressor
Metric: MAE = 12.4
Interpretation: average prediction misses by 12.4 units
Weakness: larger errors for high-value customers

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➡️ 04-classification-evaluation