Interpreting scikit-learn’s decision tree and random forest predictions
Package for interpreting scikit-learn’s decision tree and random forest predictions. Allows decomposing each prediction into bias and feature contribution components as described in http://blog.datadive.net/interpreting-random-forests/. For a dataset with n features, each prediction on the dataset is decomposed as prediction = bias + feature_1_contribution + ... + feature_n_contribution.
It works on scikit-learn’s
- DecisionTreeRegressor
- DecisionTreeClassifier
- ExtraTreeRegressor
- ExtraTreeClassifier
- RandomForestRegressor
- RandomForestClassifier
- ExtraTreesRegressor
- ExtraTreesClassifier
Free software: BSD license
Dependencies
Installation
The easiest way to install the package is via pip:
$ pip install treeinterpreter
Usage
from treeinterpreter import treeinterpreter as ti # fit a scikit-learn's regressor model rf = RandomForestRegressor() rf.fit(trainX, trainY) prediction, bias, contributions = ti.predict(rf, testX)
Prediction is the sum of bias and