Decision Trees and Random Forests: Rule-Based Learning

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• Support Vector Machines (SVM): Optimal Decision Boundaries

• Cross-Validation: Ensuring Reliable Model Evaluation

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🧠Decision Trees and Random Forests: Rule-Based Learning

Decision Trees and Random Forests are popular machine learning algorithms known for their interpretability and robustness. They are based on rule-based learning, where decisions are made using a hierarchical structure of conditions and rules.

Decision Trees

What is a Decision Tree?
A Decision Tree is a supervised learning algorithm that splits data into subsets based on conditions, forming a tree-like structure. Each internal node represents a decision rule, branches represent outcomes, and leaf nodes represent the final output.

Key Features:

• Easy to understand and interpret.

• Can handle both categorical and numerical data.

• Prone to overfitting, especially with deep trees.

How it Works:

1. Splitting: Data is divided based on feature thresholds that maximize information gain or minimize impurity (e.g., Gini Index or Entropy).

2. Stopping Criterion: The tree stops growing when predefined conditions, such as maximum depth or minimum samples per node, are met.

3. Prediction: The tree traverses the path of conditions to arrive at the leaf node with the predicted value.

Random Forests

What is a Random Forest?
Random Forest is an ensemble learning method that builds multiple decision trees and combines their outputs to make more accurate and robust predictions.

Key Features:

• Reduces overfitting compared to a single decision tree.

• Handles missing data and large datasets efficiently.

• Provides feature importance metrics for model interpretation.

How it Works:

1. Bootstrap Aggregation (Bagging): Random subsets of the training data are used to build each tree.

2. Random Feature Selection: At each split, a random subset of features is considered to create diverse trees.

3. Prediction Aggregation: The final prediction is made by averaging (regression) or majority voting (classification) from all trees.

Applications

• Decision Trees: Customer segmentation, credit risk analysis, and diagnostic tools.

• Random Forests: Fraud detection, stock market prediction, and image classification.

Conclusion

Decision Trees offer simplicity and interpretability, while Random Forests provide robustness and accuracy. Together, they form a versatile toolkit for solving complex machine learning problems.

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