Random Forest Classifier
Powerful ensemble learning with multiple decision trees
Random Forest Configuration
More trees = better accuracy but slower
Controls tree complexity
Minimum samples required to split a node
Features to consider at each split
Ensemble Results
Training Accuracy
100.00%
OOB Score
100.00%
Feature 1 Importance
10.0%
Feature 2 Importance
90.0%
Avg Tree Depth: 1.0
Training Data
Add Data Point
Load Sample Dataset
| # | Feature 1 | Feature 2 | Label | Predicted | Action |
|---|---|---|---|---|---|
| 1 | 2 | 3 | A | A | |
| 2 | 3 | 4 | A | A | |
| 3 | 4 | 3 | A | A | |
| 4 | 8 | 7 | B | B | |
| 5 | 9 | 8 | B | B | |
| 6 | 8 | 9 | B | B | |
| 7 | 5 | 5 | A | A | |
| 8 | 6 | 6 | B | B | |
| 9 | 2.5 | 3.5 | A | A | |
| 10 | 8.5 | 7.5 | B | B |
Individual Tree Details
Tree 1
Depth: 1
Samples: 10
Accuracy: 100.0%
Split Feature: feature2
Tree 2
Depth: 1
Samples: 10
Accuracy: 100.0%
Split Feature: feature2
Tree 3
Depth: 1
Samples: 10
Accuracy: 100.0%
Split Feature: feature2
Tree 4
Depth: 1
Samples: 10
Accuracy: 100.0%
Split Feature: feature2
Tree 5
Depth: 1
Samples: 10
Accuracy: 100.0%
Split Feature: feature2
Tree 6
Depth: 1
Samples: 10
Accuracy: 100.0%
Split Feature: feature2
... and 4 more trees
Related Topics & Algorithms
Explore these related algorithms and concepts to deepen your understanding and discover complementary techniques.
Decision Tree
Base learner for random forest ensemble
XGBoost
Alternative boosting-based ensemble method
AdaBoost
Sequential ensemble with adaptive weighting
Ensemble Methods
Theory of bagging, boosting, and stacking
Feature Engineering
Feature selection using forest importance
Hyperparameter Tuning
Optimize number of trees and tree depth