Evaluating Machine Learning Models Beyond Accuracy

Accuracy is only a starting point

Accuracy can hide important failure modes, especially when classes are imbalanced or the cost of mistakes is uneven.

Metrics depend on the product

A spam filter, medical triage tool, recommendation system, and resume matcher all need different evaluation priorities.

from sklearn.metrics import precision_score, recall_score, f1_score
 
scores = {
    "precision": precision_score(y_true, y_pred),
    "recall": recall_score(y_true, y_pred),
    "f1": f1_score(y_true, y_pred),
}

Better reporting

A good project report includes a baseline, test split details, confusion matrix, and a short explanation of model limitations.