Scikit Learn

# Model Selection and Evaluation Reference

## Overview

Comprehensive guide for evaluating models, tuning hyperparameters, and selecting the best model using scikit-learn's model selection tools.

## Train-Test Split

### Basic Splitting

```python
from sklearn.model_selection import train_test_split

# Basic split (default 75/25)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)

# With stratification (preserves class distribution)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.25, stratify=y, random_state=42
)

# Three-way split (train/val/test)
X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)
X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)
```

## Cross-Validation

### Cross-Validation Strategies

**KFold**
- Standard k-fold cross-validation
- Splits data into k consecutive folds
```python
from sklearn.model_selection import KFold

kf = KFold(n_splits=5, shuffle=True, random_state=42)
for train_idx, val_idx in kf.split(X):
    X_train, X_val = X[train_idx], X[val_idx]
    y_train, y_val = y[train_idx], y[val_idx]
```

**StratifiedKFold**
- Preserves class distribution in each fold
- Use for imbalanced classification
```python
from sklearn.model_selection import StratifiedKFold

skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)
for train_idx, val_idx in skf.split(X, y):
    X_train, X_val = X[train_idx], X[val_idx]
    y_train, y_val = y[train_idx], y[val_idx]
```

**TimeSeriesSplit**
- For time series data
- Respects temporal order
```python
from sklearn.model_selection import TimeSeriesSplit

tscv = TimeSeriesSplit(n_splits=5)
for train_idx, val_idx in tscv.split(X):
    X_train, X_val = X[train_idx], X[val_idx]
    y_train, y_val = y[train_idx], y[val_idx]
```

**GroupKFold**
- Ensures samples from same group don't appear in both train and validation
- Use when samples are not independent
```python
from sklearn.model_selection import GroupKFold

gkf = GroupKFold(n_splits=5)
for train_idx, val_idx in gkf.split(X, y, groups=group_ids):
    X_train, X_val = X[train_idx], X[val_idx]
    y_train, y_val = y[train_idx], y[val_idx]
```

**LeaveOneOut (LOO)**
- Each sample used as validation set once
- Use for very small datasets
- Computationally expensive
```python
from sklearn.model_selection import LeaveOneOut

loo = LeaveOneOut()
for train_idx, val_idx in loo.split(X):
    X_train, X_val = X[train_idx], X[val_idx]
    y_train, y_val = y[train_idx], y[val_idx]
```

### Cross-Validation Functions

**cross_val_score**
- Evaluate model using cross-validation
- Returns array of scores
```python
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100, random_state=42)
scores = cross_val_score(model, X, y, cv=5, scoring='accuracy')

print(f"Scores: {scores}")
print(f"Mean: {scores.mean():.3f} (+/- {scores.std() * 2:.3f})")
```

**cross_validate**
- More comprehensive than cross_val_score
- Can return multiple metrics and fit times
```python
from sklearn.model_selection import cross_validate

model = RandomForestClassifier(n_estimators=100, random_state=42)
cv_results = cross_validate(
    model, X, y, cv=5,
    scoring=['accuracy', 'precision', 'recall', 'f1'],
    return_train_score=True,
    return_estimator=True  # Returns fitted estimators
)

print(f"Test accuracy: {cv_results['test_accuracy'].mean():.3f}")
print(f"Test precision: {cv_results['test_precision'].mean():.3f}")
print(f"Fit time: {cv_results['fit_time'].mean():.3f}s")
```

**cross_val_predict**
- Get predictions for each sample when it was in validation set
- Useful for analyzing errors
```python
from sklearn.model_selection import cross_val_predict

model = RandomForestClassifier(n_estimators=100, random_state=42)
y_pred = cross_val_predict(model, X, y, cv=5)

# Now can analyze predictions vs actual