Machine Learning · Chapter 14 of 40
Random Forest
A RANDOM FOREST is an ENSEMBLE of many decision trees, each trained on a random subset of data and features. Predictions are averaged (regression) or voted (classification).
Strong out-of-the-box performance on tabular data.
Example 1 (python)
from sklearn.ensemble import RandomForestClassifier
m = RandomForestClassifier(n_estimators=200, random_state=42).fit(X_train, y_train)
print(m.score(X_test, y_test))Output
0.91Usually beats a single tree.
Example 2 (python)
print(m.feature_importances_[:5])Output
[0.12 0.08 0.35 0.02 0.05]Built-in feature importance.
Key points
- Ensemble of many trees.
- Reduces variance vs single tree.
- Great on tabular data.
- Provides feature importances.
💡 Note: Random forests handle mixed data types and missing values gracefully with less pre-processing than most models.