Decision Trees are a non-parametric supervised learning method used for classification and regression tasks. They create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.
Random Forests are an ensemble learning method that operates by constructing multiple decision trees during training and outputting the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
- Decision tree construction and pruning
- Entropy and information gain
- Gini impurity
- Ensemble methods and bagging
- Feature importance in random forests
Exercise: Credit Risk Assessment
Using the German Credit Risk dataset:
- Preprocess the data for decision tree modeling
- Build a single decision tree classifier
- Visualize and interpret the decision tree
- Implement a random forest classifier
- Compare the performance of the decision tree and random forest
- Analyze feature importance from the random forest model
StatQuest: Decision Trees
Main resource for today
Random Forests in Python
Comprehensive guide to random forests
Visualizing Decision Trees
How to visualize decision trees in Python
Feature Importance
Interpreting feature importance in random forests
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