Test models properly.

What is Cross-Validation?

Better way to test model performance than single train/test split.

Like taking multiple practice exams instead of just one!

Why Needed?

Single split might be lucky or unlucky: - Lucky: Easy test data → Overestimate accuracy - Unlucky: Hard test data → Underestimate accuracy

K-Fold Cross-Validation

Split data into K parts, test K times:

**5-Fold Example**: 1. Use fold 1 as test, others as train 2. Use fold 2 as test, others as train 3. Use fold 3 as test, others as train 4. Use fold 4 as test, others as train 5. Use fold 5 as test, others as train

**Final score**: Average of all 5 tests

Python Code

```python from sklearn.model_selection import cross_val_score from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier()

5-fold cross-validation scores = cross_val_score(model, X, y, cv=5)

print(f"Scores: {scores}") print(f"Average: {scores.mean():.2f}") print(f"Std Dev: {scores.std():.2f}") ```

Stratified K-Fold

Keeps class proportions in each fold:

```python from sklearn.model_selection import StratifiedKFold

skf = StratifiedKFold(n_splits=5) scores = cross_val_score(model, X, y, cv=skf) ```

Leave-One-Out

Use each sample as test once:

Good for small datasets, but slow!

Time Series Split

For time-based data:

```python from sklearn.model_selection import TimeSeriesSplit

tscv = TimeSeriesSplit(n_splits=5) scores = cross_val_score(model, X, y, cv=tscv) ```

Best Practices

- Use 5 or 10 folds - Stratified for imbalanced data - Time series split for temporal data - More folds = more reliable, but slower

Remember

- More reliable than single split - Standard is 5-fold - Reports average performance