Examples#
Examples demonstrating how to use treeple algorithms.
Calibrated decision trees via honesty#
Examples demonstrating the usage of honest decision trees to obtain calibrated predictions.
Plot honest forest calibrations on overlapping gaussian simulations
Multi-view learning with Decision-trees#
Examples demonstrating multi-view learning using random forest variants.
Analyze a multi-view dataset with a multi-view random forest
Outlier-detection#
Examples concerning how to do outlier detection with decision trees.
Quantile Predictions with Random Forest#
Examples demonstrating how to generate quantile predictions using Random Forest variants.
Predicting with different quantile interpolation methods
Quantile prediction intervals with Random Forest Regressor
Quantile prediction with Random Forest Regressor class
Quantile regression with oblique regression forest
Comparing sklearn and treeple decision trees#
Examples demonstrating the difference between sklearn and treeple decision trees.
Plot the decision surface of decision trees trained on the iris dataset
Sparse oblique projections with oblique decision-trees#
Examples demonstrating learning using oblique random forests.
Compare extra oblique forest and oblique random forest predictions on cc18 datasets
Speed of Extra Oblique Random Forest vs Oblique Random Forest on different dataset sizes
Plot oblique forest and axis-aligned random forest predictions on sparse parity simulation
Compare the decision surfaces of oblique extra-trees with standard oblique trees
Plot oblique forest and axis-aligned random forest predictions on cc18 datasets
Decision-tree splitters#
Examples demonstrating different node-splitting strategies for decision trees.
Demonstrate and visualize a multi-view projection matrix for an axis-aligned tree
Plot the projection matrices of an oblique tree for sampling images, or time-series
Plot the sparse projection matrices of an oblique tree
Treeple for Hypothesis Testing#
Examples concerning how to use treeple as hypothesis test tools. Tutorials include estimating true statistics with true posterior functionss, using forest to calculate statistic estimates, and calculating p-values.
