Random Forest Cheat Sheet

A cheat sheet for Random Forest covering bagging, feature randomness, out-of-bag scoring, hyperparameter tuning, and feature importance in scikit-learn.

2 PagesIntermediateMar 2, 2026

Classifier with scikit-learn

Fit a forest and check its out-of-bag score.

python

from sklearn.ensemble import RandomForestClassifierrf = RandomForestClassifier(    n_estimators=300, max_depth=None, max_features='sqrt',    min_samples_leaf=2, oob_score=True, n_jobs=-1, random_state=42)rf.fit(X_train, y_train)print('OOB score:', rf.oob_score_)        # validation-free accuracy estimateprint('Test accuracy:', rf.score(X_test, y_test))

Regressor & Feature Importance

Random Forest for regression tasks.

python

from sklearn.ensemble import RandomForestRegressorreg = RandomForestRegressor(n_estimators=200, n_jobs=-1, random_state=42)reg.fit(X_train, y_train)importances = reg.feature_importances_   # mean decrease in impurity per feature

Hyperparameter Search

Tune key forest hyperparameters.

python

from sklearn.model_selection import RandomizedSearchCVparam_dist = {    'n_estimators': [100, 300, 500],    'max_depth': [None, 10, 20, 30],    'min_samples_leaf': [1, 2, 4],}search = RandomizedSearchCV(RandomForestClassifier(), param_dist, cv=5, n_iter=20, n_jobs=-1)search.fit(X_train, y_train)print(search.best_params_)

Key Concepts

Core theory behind Random Forest.

Bagging- Each tree trains on a bootstrap sample (random sample with replacement) of the training data
Feature randomness- Each split considers only a random subset of features (max_features), decorrelating the trees
Out-of-bag (OOB) score- Free validation estimate using the roughly 37% of samples excluded from each tree's bootstrap sample
n_estimators- Number of trees in the forest; more trees reduce variance with diminishing returns on compute
Feature importance- Mean decrease in impurity across all trees, or more robust permutation importance

Pro Tip

Prefer permutation_importance from sklearn.inspection over the default feature_importances_ when features vary in cardinality or scale — impurity-based importance is biased toward high-cardinality and continuous features, even when they're not truly predictive.

Was this cheat sheet helpful?

Decision Trees Cheat Sheet

Gradient Boosting Cheat Sheet

Explore Topics

#RandomForest#RandomForestCheatSheet#DataScience#Intermediate#ClassifierWithScikitLearn#RegressorFeatureImportance#HyperparameterSearch#KeyConcepts#MachineLearning#CheatSheet#SkillVeris

Sri Hayavadhana Info-Tech

Professional Web Designing Services