3.2 Decision Trees and Random Forests

Decision trees and random forests are powerful supervised learning algorithms used for classification and regression tasks. These methods create hierarchical structures to make predictions based on input features, offering interpretability and versatility in handling various data types.

Random forests, an ensemble learning technique, build multiple decision trees to improve accuracy and reduce overfitting. By introducing randomness through bootstrap sampling and feature selection, random forests create robust models capable of tackling complex machine learning problems across diverse domains.

Decision trees and Random forests

Tree structure and principles

Top images from around the web for Tree structure and principles

• 

  Using a Decision Tree | Principles of Management View original

  Is this image relevant?

• 

  Decision Trees for Machine Learning View original

  Is this image relevant?

• 

  Decision Trees for Machine Learning View original

  Is this image relevant?

• 

  Using a Decision Tree | Principles of Management View original

  Is this image relevant?

• 

  Decision Trees for Machine Learning View original

  Is this image relevant?

1 of 3

Top images from around the web for Tree structure and principles

• 

  Using a Decision Tree | Principles of Management View original

  Is this image relevant?

• 

  Decision Trees for Machine Learning View original

  Is this image relevant?

• 

  Decision Trees for Machine Learning View original

  Is this image relevant?

• 

  Using a Decision Tree | Principles of Management View original

  Is this image relevant?

• 

  Decision Trees for Machine Learning View original

  Is this image relevant?

1 of 3

• Decision trees create hierarchical, tree-like structures for classification and regression tasks
• Structure components include nodes (decision points), branches (possible outcomes), and leaf nodes (final predictions)
• Recursive partitioning algorithm splits data based on features providing most information gain
• Models handle both numerical and categorical data (age, income, color, shape)
• Interpretable models allow easy visualization of decision-making process

Random forest fundamentals

• Ensemble learning method constructs multiple decision trees
• Combines predictions to improve accuracy and reduce overfitting
• Introduces randomness through bootstrap sampling of training data
• Implements random feature selection at each split
• Versatile for various machine learning problems (image classification, customer churn prediction)

Building and interpreting decision trees

Construction process

• Select best feature to split on each node using metrics
  • Gini impurity
  • Entropy
  • Mean squared error
• For classification, predict class label by following path from root to leaf node
  • Assign majority class as prediction
• For regression, predict continuous values by averaging target values of training instances at leaf node
• Pruning techniques prevent overfitting
  • Cost-complexity pruning removes branches not significantly improving performance
• Key hyperparameters affect model complexity and generalization
  • Tree depth
  • Minimum samples required to split node

Interpretation and analysis

• Calculate feature importance based on total reduction of impurity or error across all nodes
• Analyze tree structure, split conditions, and leaf node predictions
• Identify key features influencing decisions
• Visualize decision tree to understand overall model behavior (graphviz, sklearn.tree.plot_tree)

Ensemble methods for decision trees

Bagging and random forests

• Create multiple subsets of training data through random sampling with replacement
• Train separate model on each subset
• Random forests use decision trees as base models
• Incorporate random feature selection in random forests
• Reduce correlation between individual trees
• Provide natural way to estimate feature importance

Boosting algorithms

• Build sequence of weak learners focusing on misclassified instances from previous iterations
• Popular boosting algorithms use decision trees as base learners
  • AdaBoost
  • Gradient Boosting Machines (GBM)
  • XGBoost
• Optimize differentiable loss function
• Stacking combines predictions from multiple models using meta-learner for final prediction

Evaluation and tuning

• Assess performance using techniques
  • Out-of-bag error estimation
  • Cross-validation
• Adjust hyperparameters to optimize random forest performance
  • Number of trees
  • Maximum depth
  • Number of features considered for each split
• Implement parallel processing for faster training on large datasets

Random forests vs individual decision trees

Advantages of random forests

• Reduce overfitting by averaging predictions from multiple decorrelated trees
• Improve generalization and model robustness
• Decrease correlation between individual trees through random feature selection
• Handle high-dimensional data effectively (genomic data analysis, text classification)
• Less sensitive to outliers compared to individual decision trees

Performance improvements

• Provide natural way to estimate feature importance by aggregating scores across all trees
• Use out-of-bag (OOB) samples for unbiased error estimation
• Calculate feature importance without separate validation set
• Easily implement parallel processing for faster training
  • Individual trees built independently

Practical considerations

• Tuning hyperparameters crucial for optimal performance
  • Number of trees (typically 100-1000)
  • Maximum depth (controls model complexity)
  • Minimum samples per leaf (prevents overfitting)
• Trade-off between model complexity and interpretability
  • Random forests less interpretable than single decision tree
  • Provide feature importance rankings for overall model understanding