Classification and Regression Trees (CART)
Classification and Regression Trees (CART) is a statistical technique used for classification and regression predictive modeling problems. Developed by Leo Breiman, Jerome Friedman, Richard A. Olshen, and Charles J. Stone in 1984, CART algorithms are fundamental tools in machine learning, particularly in decision tree learning.
History
- CART was introduced in the book "Classification And Regression Trees" published in 1984, which laid down the theoretical foundations for the algorithm.
- The technique was developed as an extension of earlier work in decision tree algorithms, aiming to provide a more systematic approach to tree construction.
Mechanism
CART builds decision trees by:
- Splitting: The process of choosing the best predictor to divide the data into two groups, maximizing the homogeneity within each group. For classification, this often involves measures like Gini impurity or information gain, whereas for regression, variance reduction is commonly used.
- Pruning: After a tree is grown, it can be pruned to reduce complexity and avoid overfitting by removing branches that contribute little to the overall prediction accuracy.
- Tree Growth: The tree grows by recursively splitting nodes until a stopping criterion is met, such as reaching a maximum depth, minimum number of samples per leaf, or until all instances in a node belong to the same class or have similar regression values.
Applications
CART is widely used in:
- Finance: For credit scoring, customer segmentation, and fraud detection.
- Healthcare: In predicting patient outcomes, disease diagnosis, and medical research.
- Marketing: For customer churn prediction, market basket analysis, and campaign optimization.
- Manufacturing: To predict equipment failures or optimize production processes.
Advantages
- Easy to understand and interpret.
- Can handle both categorical and numerical data.
- Non-parametric and does not assume a linear model.
- Can capture non-linear relationships in data.
Limitations
- Prone to overfitting if not properly pruned or if the tree is too deep.
- Can be unstable, where small changes in data might result in a different tree structure.
- Does not handle missing data well without preprocessing.
Extensions and Variations
Over time, several enhancements and variations of CART have been developed:
- Random Forests - An ensemble method that uses multiple decision trees.
- Gradient Boosting - A technique that builds trees in a stage-wise fashion to minimize a loss function.
- C4.5 Algorithm - Another decision tree algorithm that improves upon ID3 by handling continuous attributes, missing data, and pruning.
External Links
See Also