Package for 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
Free software: BSD license
The easiest way to install the package is via pip::
$ pip install treeinterpreter
::
from treeinterpreter import treeinterpreter as ti
rf = RandomForestRegressor() rf.fit(trainX, trainY)
prediction, bias, contributions = ti.predict(rf, testX)
Prediction is the sum of bias and feature contributions::
assert(numpy.allclose(prediction, bias + np.sum(contributions, axis=1))) assert(numpy.allclose(rf.predict(testX), bias + np.sum(contributions, axis=1)))
More usage examples at http://blog.datadive.net/random-forest-interpretation-with-scikit-learn/.
FAQs
Package for interpreting scikit-learn's decision tree and random forest predictions.
We found that treeinterpreter demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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