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Machine Learning Evaluation Metrics

Choose metrics according to the ML problem and business objective.

Regression Metrics

Metric	Meaning
MAE	Average absolute prediction error
MSE	Average squared error; penalizes large errors
RMSE	Square root of MSE, in target units
R²	Proportion of target variance explained by the model

from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score

mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
rmse = mse ** 0.5
r2 = r2_score(y_test, y_pred)

Classification Metrics

Metric	Typical use
Accuracy	Overall correctness
Precision	False positives are costly
Recall	False negatives are costly
F1 Score	Balance of precision and recall
ROC-AUC	Class discrimination across thresholds

from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
f1 = f1_score(y_test, y_pred)

Confusion Matrix

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

Remember: Select the metric based on the problem, class balance and cost of errors.

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