Introduction to Decision Trees

Study Notes

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

Description

Explore the fundamentals of decision trees, a supervised machine learning algorithm used for classification and regression tasks. Learn how these models make predictions by recursively partitioning data based on feature decisions, utilizing criteria like Gini impurity and entropy. This quiz will test your understanding of decision tree mechanics and their practical applications.