Ai Decision Trees — Free AI & ML Tutorial
Learn Ai Decision Trees in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Ai Decision Trees in AI & ML with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Ai Decision Trees in AI & ML
A Decision Tree is a flowchart-like model that makes decisions by splitting data based on feature values. It starts at a "root" node, then asks a series of yes/no questions to reach a final decision (leaf node).
Example: Predicting if a student will pass — first split on attendance (>75%?), then marks (>50?), then homework submitted (yes/no). Each path through the tree gives a different prediction.
Random Forest is an ensemble of many decision trees. Each tree is trained on a random subset of data and features. The final prediction is a vote from all trees. This reduces overfitting and improves accuracy dramatically.
Decision Trees are extremely popular because they are: interpretable (you can see exactly why a decision was made), fast, handle both numerical and categorical data, and require no feature scaling.
Ai Decision Trees — Syntax
# Decision Tree logic (conceptual): # Root: Is attendance > 75%? # YES → Is marks > 60? → YES → PASS, NO → borderline # NO → Is marks > 80? → YES → borderline, NO → FAIL # # Random Forest: 100 trees vote → majority wins
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