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Decision Tree Analyzer
Learn how decision trees split data using entropy and information gain
Split Configuration
Split: feature1 ≤ 5 vs feature1 > 5
Results
Parent Entropy: 1.0000
Information Gain: 1.0000
Quality: Excellent Split
Split Distribution
Left Branch (feature1 ≤ 5): 4 samples
Entropy: 0.0000
Labels: A, A, A, A
Right Branch (feature1 > 5): 4 samples
Entropy: 0.0000
Labels: B, B, B, B
Dataset (8 samples)
Add New Data Point
| # | Feature 1 | Feature 2 | Label | Action |
|---|---|---|---|---|
| 1 | 2 | 3 | A | |
| 2 | 3 | 4 | A | |
| 3 | 4 | 3 | A | |
| 4 | 8 | 7 | B | |
| 5 | 9 | 8 | B | |
| 6 | 8 | 9 | B | |
| 7 | 5 | 5 | A | |
| 8 | 6 | 6 | B |
Sample Datasets
Related Topics & Algorithms
Explore these related algorithms and concepts to deepen your understanding and discover complementary techniques.
Ensemble Learning
Random Forest
Ensemble of decision trees for better accuracy
Ensemble Learning
XGBoost
Gradient boosted trees for superior performance
Ensemble Learning
AdaBoost
Adaptive boosting of weak decision trees
Ensemble Learning
Ensemble Methods
Combine multiple trees for robust predictions
Preprocessing
Feature Engineering
Feature importance and selection for trees
Model Evaluation
Cross Validation
Prevent overfitting in decision trees