Model Evaluation

Overview

The wrong metric on the wrong split produces confident, wrong conclusions. Evaluation is about choosing a metric that matches the business cost, validating it on a split that mirrors production, and reporting it honestly with uncertainty.

When to use

• Selecting how to score a model.
• A model "looks great" but you're unsure it's real.
• Comparing candidate models for promotion.

Metric selection

Cross-validation strategy

• Default: StratifiedKFold for classification.
• Time series: TimeSeriesSplit — never shuffle; train on past, validate on future.
• Grouped data (multiple rows per user): GroupKFold so the same group never spans train and test.
• Small data: repeated CV; report mean ± std.

Honest reporting

from sklearn.model_selection import cross_val_score, StratifiedKFold
import numpy as np

cv = StratifiedKFold(5, shuffle=True, random_state=42)
scores = cross_val_score(model, X, y, cv=cv, scoring="average_precision")
print(f"PR-AUC: {scores.mean():.3f} ± {scores.std():.3f}")  # always report spread

Beyond a single number

• Confusion matrix / classification report at the chosen threshold — accuracy hides per-class failure.
• Threshold tuning — default 0.5 is rarely optimal; pick it from the PR curve to match precision/recall needs.
• Calibration — CalibratedClassifierCV or reliability curves when probabilities feed decisions.
• Slice metrics — evaluate on subgroups to catch fairness/robustness gaps.

Pitfalls

• Accuracy on imbalanced data — 99% accuracy by predicting the majority class. Use PR-AUC/recall.
• Tuning the threshold on the test set — do it on validation.
• Shuffling time-series CV — leaks the future, inflates scores, collapses in production.
• Reporting a point estimate with no variance — a 0.2% gain inside ±1.5% noise is not a gain.
• Evaluating on the data you tuned on — keep a final untouched test set.

Hand-off

A defensible metric + validated score with uncertainty, plus a chosen decision threshold, for experiment-tracking to log and stakeholders to trust.