Home / Topics / Random Forest

Random Forest

Description

Random Forest is an ensemble learning method used for both classification and regression tasks. It builds multiple decision trees during training and outputs the mode (for classification) or mean (for regression) of the individual trees’ predictions.

How Random Forest Works

Random Forest combines the predictions of several decision trees built on randomly sampled data and features. This randomness increases the model's robustness and generalization, reducing the risk of overfitting.

Key characteristics:

  • Uses bagging (Bootstrap Aggregating) to train each tree on different subsets of the data
  • Each tree considers a random subset of features for splitting at each node
  • Reduces variance and improves accuracy over individual decision trees

Examples

Python Code for Random Forest Classifier

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load data
iris = load_iris()
X = iris.data
y = iris.target

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# Predict and evaluate
y_pred = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred):.2f}")

Real-World Applications

Random Forest Applications

  • Healthcare: Predicting disease, patient risk classification
  • Finance: Credit scoring, fraud detection, stock market analysis
  • Retail: Customer behavior prediction, recommendation engines
  • Cybersecurity: Intrusion detection, malware classification
AI risk prediction

Resources

The following resources will be manually added later:

Video Tutorials

YouTube video link.

PDF/DOC Materials

Drive links for PDF/DOC files .

Interview Questions

1. What is a Random Forest?

Show Answer

Random Forest is an ensemble method that builds multiple decision trees and combines their results to make more accurate and stable predictions. It reduces overfitting by introducing randomness into the model training process.

2. How is a Random Forest different from a Decision Tree?

Show Answer

A Decision Tree is a single predictive model that may overfit the training data. A Random Forest is an ensemble of decision trees that reduces overfitting and improves accuracy by averaging results.

3. What are the advantages of using Random Forest?

Show Answer

Advantages include high accuracy, robustness to overfitting, ability to handle missing data, and automatic feature importance estimation.

4. How does Random Forest handle overfitting?

Show Answer

By averaging predictions from many trees trained on random subsets of data and features, Random Forest reduces the variance of the final model, thereby mitigating overfitting.

5. What are hyperparameters in Random Forest?

Show Answer

Important hyperparameters include:

  • n_estimators: Number of trees in the forest
  • max_depth: Maximum depth of each tree
  • max_features: Number of features to consider at each split
  • min_samples_split: Minimum samples required to split an internal node

Back to Topics