Build and visualize decision trees for classification!
A Decision Tree is a flowchart-like structure where each internal node represents a test on a feature, each branch represents the outcome of that test, and each leaf node represents a class label. It's one of the most intuitive machine learning algorithms.
1. Start at Root: Begin with all training data at the root node
2. Find Best Split: Calculate information gain or Gini impurity for all features
3. Split Data: Divide data based on the feature with highest information gain
4. Repeat: Recursively apply steps 2-3 to child nodes
5. Stop: When reaching pure leaf nodes or maximum depth
Information Gain: Measure of how much uncertainty is reduced by splitting
Gini Impurity: Measure of how often a randomly chosen element would be incorrectly labeled
Entropy: Measure of disorder or uncertainty in data
Pruning: Removing branches to prevent overfitting
• Easy to understand and interpret
• Requires little data preparation
• Can handle both numerical and categorical data
• Can model non-linear relationships
• Feature importance is built-in
• Medical diagnosis (symptom-based diagnosis)
• Credit approval (assess creditworthiness)
• Customer segmentation (marketing strategies)
• Fraud detection (identify suspicious transactions)
• Product recommendation (suggest products)