Introduction to Random Forest

Introduction

Random forest trains many decision trees on bootstrap samples and random feature subsets, then aggregates their votes. Bagging reduces variance compared with a single deep tree.

Understanding the topic

Why ensembles help Individual trees overfit; averaging smooths decision boundaries.

Key knobs n_estimators, max_depth, max_features, min_samples_leaf.

• Why ensembles help — Individual trees overfit; averaging smooths decision boundaries.
• Key knobs — n_estimators, max_depth, max_features, min_samples_leaf.

Step-by-step explanation

1. Why ensembles help — Individual trees overfit; averaging smooths decision boundaries.
2. Key knobs — n_estimators, max_depth, max_features, min_samples_leaf.

Informative example

Python starter:

from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris

X, y = load_iris(return_X_y=True)
clf = RandomForestClassifier(n_estimators=100, random_state=0)
clf.fit(X, y)
print(clf.score(X, y))

1.0

Execution workflow

Best practices

• Hold out a test set before hyperparameter tuning.
• Scale numeric columns for distance-based models.
• Track multiple metrics — not accuracy alone on skewed labels.

Common mistakes

• Leaking test statistics into preprocessing fit on full data.
• Training on the same rows you report as test performance.
• Chasing complex models before a simple baseline.

Hands-on exercise

Practice:

• Compare feature importances
• Plot validation score vs n_estimators

Summary

Introduction to Random Forest — Bagged trees with random feature subsampling.