Q16Big Data Analytics

A Decision Tree is a highly interpretable, non-parametric Supervised Machine Learning algorithm used for both Classification (predicting categories) and Regression (predicting numbers). It completely abandons the "black-box" nature of Neural Networks, instead creating a massive mathematical flowchart that human engineers can physically read and audit.

The algorithm does not guess; it uses brutal statistical mathematics to build the tree.

• The Root Node: The algorithm analyzes a dataset of 1 million customers. It must find the absolute best feature (e.g., "Age" or "Income") to split the data into two highly distinct groups (e.g., "Will Default on Loan" vs "Will Pay").
• The Math (Entropy & Information Gain): It mathematically calculates the Entropy (chaos/impurity) of the dataset. It then tests every single variable to see which split causes the most massive drop in Entropy. This maximum drop is called the Information Gain.
• The Splitting: If "Income < $50,000" yields the highest Information Gain, that becomes the Root Node. The data is violently sliced in half.
• Recursion: The algorithm recursively repeats this exact mathematical process on the new branches, creating internal nodes (further questions) until it reaches a Leaf Node (the final absolute prediction).

• Merits: Zero requirement for data normalization. It mathematically handles missing values and nonlinear relationships effortlessly. Absolute human interpretability for legal/compliance reasons.
• Demerits (Overfitting): A Decision Tree will aggressively keep growing until it perfectly memorizes every single row in the training data, capturing random noise instead of real patterns. In production, this overfitted tree will fail catastrophically. It MUST be mathematically controlled using "Pruning" (forcing a maximum depth limit) or by combining thousands of trees into a Random Forest architecture.