# Examples using scikit-tree#

To be able to effectively use scikit-tree, look at some of the examples here to learn everything you need!

# Examples#

Examples demonstrating how to use scikit-tree 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

## Hypothesis testing with decision trees#

Examples demonstrating how to use decision-trees for statistical hypothesis testing.

Co-MIGHT when Data Exhibits Conditional Independence

Compute partial AUC using Mutual Information for Genuine Hypothesis Testing (MIGHT)

Compute partial AUC using multi-view MIGHT (MV-MIGHT)

Demonstrate Conditional Mutual Information for Genuine Hypothesis Testing (Co-MIGHT)

Mutual Information for Genuine Hypothesis Testing (MIGHT)

Mutual Information for Genuine Hypothesis Testing (MIGHT) with Imbalanced Data

## 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.

ExtendedIsolationForest example

## 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 sktree decision trees#

Examples demonstrating the difference between sklearn and sktree 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

Compare the decision surfaces of oblique extra-trees with standard oblique trees

Plot oblique forest and axis-aligned random forest predictions on cc18 datasets

Plot oblique forest and axis-aligned random forest predictions on sparse parity simulation

Speed of Extra Oblique Random Forest vs Oblique Random Forest on different dataset sizes

## 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.

0: Estimating true posteriors & statistics

1-1d: Calculating Hellinger Distance

1-2: Calculating p-value (MIGHT)

2-1a: Calculating S@98 with multiview data

2-2: Calculating p-value with multiview data (CoMIGHT)