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A fast and frugal tree classifier for sklearn

Project description


A fast-and-frugal-tree classifier based on Python's scikit learn.

Fast-and-frugal trees are classification trees that are especially useful for making decisions under uncertainty. Due their simplicity and transparency they are very robust against noise and errors in data. They are one of the heuristics proposed by Gerd Gigerenzer in Fast and Frugal Heuristics in Medical Decision. This particular implementation is based on on the R package FFTrees, developed by Phillips, Neth, Woike and Grassmaier.


You can install fasttrees using

pip install fasttrees


Instantiate a fast-and-frugal tree classifier:

from fasttrees.fasttrees import FastFrugalTreeClassifier
fc = FastFrugalTreeClassifier()

Fit on your data:, y_train)

View the fitted tree (this is especially useful if the 'predictions' will be carried out by humans in practice):



preds = fc.predict(X_test)


fc.score(X_test, y_test)

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