Introduction to Decision Trees

Introduction to Decision Trees

• Decision trees are a supervised machine learning algorithm used for classification and regression tasks.
• They model a target variable prediction using simple rules derived from data features.
• The model is a tree-like structure, where each node is a feature, each branch is a decision rule, and each leaf node represents a prediction.

How Decision Trees Work

• The algorithm recursively divides data into smaller sets based on features.
• At each node, the algorithm selects the best feature for data splitting to maximize target variable homogeneity within subsets.
• Common splitting criteria are Gini impurity and entropy.
• Gini impurity quantifies the probability of misclassifying a random data point in a subset.
• Entropy measures uncertainty or randomness in a subset.
• The algorithm stops when a stopping criterion is met, such as maximum depth, minimum samples per leaf, or minimum impurity reduction.

Advantages of Decision Trees

• Easy to understand and interpret through visual tree diagrams.
• Relatively simple to implement.
• Handles numerical and categorical data.
• Non-parametric, not assuming a specific data distribution.
• Useful for feature importance analysis.
• Requires minimal data preprocessing (though careful handling of missing values is important).

Disadvantages of Decision Trees

• Prone to overfitting if the tree grows too deep.
• Instability; small data changes can result in significantly different trees.
• Less accurate than some algorithms for complex datasets.
• Can be computationally expensive for large datasets.

Types of Decision Trees

• Classification Trees: Predict categorical target variables.
• Regression Trees: Predict continuous target variables.

Key Concepts in Decision Trees

• Root Node: The top node, representing the entire dataset.
• Internal Nodes: Nodes representing features used for data splitting.
• Leaf Nodes: Nodes representing final predictions.
• Branches: Segments connecting nodes, representing decision rules.
• Pruning: A technique to reduce overfitting by trimming parts of the tree. Methods include pre-pruning (stopping early) and post-pruning (removing branches later).

Evaluation Metrics

• Accuracy: Percentage of correctly classified instances.
• Precision: Proportion of correctly predicted positive cases.
• Recall: Proportion of actual positive cases correctly identified.
• F1-score: Harmonic mean of precision and recall, considering both false positives and negatives.

Important Considerations

• Handling Missing Values: Imputation (estimating missing values) or removing instances with missing values.
• Feature Scaling: Improves performance in some cases, especially with certain splitting criteria.

Applications of Decision Trees

• Medical diagnoses
• Customer churn prediction
• Credit risk assessment
• Fraud detection
• Financial forecasting
• Image classification