/ˈrændəm fɔːrɪst/
noun … “many trees, one wise forest.”
Random Forest is an ensemble machine learning method that builds multiple Decision Trees and aggregates their predictions to improve accuracy, robustness, and generalization. Each tree is trained on a bootstrap sample of the data with a randomly selected subset of features, introducing diversity and reducing overfitting compared to a single tree. The ensemble predicts outcomes by majority vote for classification or averaging for regression, leveraging the wisdom of the crowd among trees.
Mathematically, if {T₁, T₂, ..., Tₙ} are individual decision trees, the Random Forest prediction for a data point x is:
ŷ = majority_vote(T₁(x), T₂(x), ..., Tₙ(x)) // classification
ŷ = mean(T₁(x), T₂(x), ..., Tₙ(x)) // regressionRandom Forest interacts naturally with several statistical and machine learning concepts. It relies on bootstrap resampling for generating diverse training sets, Variance reduction through aggregation, Information Gain or Gini Impurity for splitting nodes, and feature importance measures to identify predictive variables. Random Forests are widely applied in classification tasks like medical diagnosis, fraud detection, and image recognition, as well as regression problems in finance, meteorology, and resource modeling.
Example conceptual workflow for a Random Forest:
collect dataset with predictor and target variables
generate multiple bootstrap samples of the dataset
for each sample, train a Decision Tree using randomly selected features at each split
aggregate predictions from all trees via majority vote or averaging
evaluate ensemble performance on test data and adjust hyperparameters if neededIntuitively, a Random Forest is like consulting a council of wise trees: each tree offers an opinion based on its own limited view of the data, and the ensemble combines these perspectives to form a decision that is more reliable than any individual tree. It transforms the variance and unpredictability of single learners into a stable, robust predictive forest.