Classification Tree Analysis [Neurosurgery Wiki]

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====== Classification Tree Analysis ======
 

Classification Tree Analysis (CTA) is a decision-tree-based statistical method used for classifying observations into different groups based on predictor variables. It is widely used in machine learning, medical diagnosis, risk assessment, and various predictive analytics applications.

### **Key Concepts of Classification Tree Analysis:**

1. **Tree Structure:**
   - A classification tree consists of a root node, internal nodes (decision nodes), and leaf nodes (terminal nodes).
   - Each internal node represents a decision based on a feature (predictor variable).
   - Each leaf node represents a class label (target variable).

2. **Splitting Criteria:**
   - The tree grows by splitting the data at each node based on the predictor variable that provides the most separation between classes.
   - Common splitting criteria:
     - **Gini Impurity**: Measures how often a randomly chosen element would be incorrectly classified.
     - **Entropy (Information Gain)**: Measures the reduction in disorder (uncertainty) after a split.

3. **Pruning:**
   - Trees can become too complex and overfit the training data.
   - Pruning simplifies the tree by removing branches that provide little predictive power.
   - Two types of pruning:
     - **Pre-pruning (early stopping)**: Stops tree growth when certain conditions are met (e.g., minimum number of samples in a node).
     - **Post-pruning (cost complexity pruning)**: Removes nodes after the tree is fully grown to improve generalization.

4. **Advantages:**
   - Easy to interpret and visualize.
   - Handles categorical and numerical data.
   - Requires minimal data preprocessing (e.g., no need for feature scaling).

5. **Disadvantages:**
   - Prone to overfitting, especially with deep trees.
   - Sensitive to small changes in the data.
   - Less accurate than ensemble methods (e.g., Random Forest, Gradient Boosting).