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Machine Learning Tutorial

Supervised learning, classification, neural networks and core ML algorithms.

Random Forest

An ensemble of many decision trees, each trained on a random subset of data and features — the forest votes (classification) or averages (regression) to get the final prediction.

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)
  • Reduces overfitting compared to a single decision tree (variance reduction via averaging)
  • Bagging — each tree sees a bootstrapped random sample of the training data
  • Provides a built-in feature_importances_ ranking