4.1.2. Classification Algorithms (Decision Trees, Random Forest, SVM)

First Principle: Classification algorithms fundamentally learn to assign discrete categories or labels to new data points by modeling the relationships between input features and known categorical outcomes.

Classification algorithms are a subset of supervised learning used when the target variable is a discrete category or class (e.g., "spam" or "not spam", "fraud" or "not fraud", "cat" or "dog").

Key Classification Algorithms and their characteristics:

• Decision Trees:
  • What it is: A tree-like model where each internal node represents a test on a feature, each branch represents an outcome of the test, and each leaf node represents a class label.
  • Strengths: Easy to understand and interpret ("white box" model), handles both numerical and categorical data, doesn't require feature scaling.
  • Limitations: Prone to overfitting, can be unstable (small changes in data can lead to large changes in tree structure).
• Random Forest:
  • What it is: An ensemble learning method that constructs a multitude of decision trees at training time and outputs the class that is the mode of the classes (classification) or mean prediction (regression) of the individual trees.
  • Strengths: Reduces overfitting compared to single decision trees, high accuracy, robust to noise and outliers, provides feature importance.
  • AWS: SageMaker Random Forest is a built-in algorithm.
• Support Vector Machines (SVM):
  • What it is: A powerful algorithm that finds the optimal hyperplane that best separates data points into different classes with the largest possible margin.
  • Strengths: Effective in high-dimensional spaces, can use kernel trick for non-linear separation.
  • Limitations: Computationally intensive for large datasets, sensitive to feature scaling, "black box" model.
• XGBoost (Extreme Gradient Boosting): (Also used for classification.)
  • What it is: Highly effective for classification due to its sequential tree building, strong regularization, and optimization for performance.
  • AWS: SageMaker XGBoost.
• Metrics for Classification: Accuracy, Precision, Recall, F1-Score, ROC-AUC, Confusion Matrix.

Scenario: You need to build a model to classify customer reviews as "positive," "negative," or "neutral" sentiment. You want a robust model that avoids overfitting and provides good generalization on new reviews.

Reflection Question: How do classification algorithms like Decision Trees, Random Forest, or XGBoost fundamentally learn to assign discrete categories or labels to new data points by modeling the relationships between input features and known categorical outcomes, considering their strengths and weaknesses (e.g., overfitting, interpretability)?