โš ๏ธ This is an AI-generated test article created solely to exercise the theme (features, layouts, code collapse, TOC). It is synthetic demo content, not real research.

Why evaluation matters

A model is only as good as the metric you use to judge it. Accuracy alone hides problems in imbalanced datasets. Here is a quick checklist.

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import numpy as np
from sklearn.metrics import precision_recall_fscore_support

y_true = np.array([0, 1, 1, 0, 1, 0, 1, 1, 0, 1])
y_pred = np.array([0, 1, 0, 0, 1, 0, 1, 1, 0, 1])

p, r, f, _ = precision_recall_fscore_support(y_true, y_pred, average="binary")
print(f"precision={p:.2f} recall={r:.2f} f1={f:.2f}")

Choosing metrics

For classification, always report precision, recall and F1 alongside accuracy. For regression, use MAE and RMSE rather than a single Rยฒ.

A common pitfall

Report the distribution of errors, not just an average. Averaging hides bimodal failures.

Key takeaway

Validation should reflect real-world performance, never the training split.