Growing a Two-Level Tree: Leaves & Nested IF Predictions
This lesson is part of the course
In this lesson
You grow Kestrel's first real tree: DTI at the root, income on the left, employment on the right. You fill the four leaves, turn the tree into one nested IF and measure its training accuracy.
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Lessons in this course
- Entropy & Gini: How Mixed Is a Node? Locked
- Information Gain: Scoring Candidate Splits with COUNTIFS Locked
- The Best Numeric Threshold: Trying Every Cut-Point Locked
- Growing a Two-Level Tree: Leaves & Nested IF Predictions Locked
- Overfitting & Pruning: Train vs Test Accuracy by Depth Locked
- Bagging & Random Forests: Majority Vote and Out-of-Bag Rows Locked
- Gradient Boosting by Hand: Residuals, Stumps & a Learning Rate Locked
- Feature Importance from Impurity Decrease Locked