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.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
In this segment of the Risky Business podcast, Feross Aboukhadijeh and Patrick Gray discuss the challenges of tracking malware discovered in open source softare.
Research
Security News
A threat actor's playbook for exploiting the npm ecosystem was exposed on the dark web, detailing how to build a blockchain-powered botnet.
Security News
NVD’s backlog surpasses 20,000 CVEs as analysis slows and NIST announces new system updates to address ongoing delays.