fasttrees

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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.

Install

You can install fasttrees using pip install fasttrees

Quick first start

Below we provide a qick first start example with fast-and-frugal trees. We use the popular iris flower data set (also known as the Fisher's Iris data set), split it into a train and test data set, and fit a fast-and-frugal tree classifier on the training data set. Finally, we get the score on the test data set.

```python from sklearn import datasets, model_selection

from fasttrees import FastFrugalTreeClassifier

Load data set

irisdict = datasets.loadiris(as_frame=True)

Load training data, preprocess it by transforming y into a binary classification problem, and

split into train and test data set

Xiris, yiris = irisdict['data'], irisdict['target'] yiris = yiris.apply(lambda entry: entry in [0, 1]).astype(bool) Xtrainiris, Xtestiris, ytrainiris, ytestiris = modelselection.traintestsplit( Xiris, yiris, testsize=0.4, random_state=42)

Fit and test fitted tree

fftc = FastFrugalTreeClassifier() fftc.fit(Xtrainiris, ytrainiris) fftc.score(Xtestiris, ytestiris) ```

Licensing

Copyright (c) 2019-2024 Dominic Zijlstra, Stefan Bachhofner

Licensed under the MIT (SPDX short identifier: MIT) (the "License"); you may not use this file except in compliance with the License.

You may obtain a copy of the License by reviewing the file LICENSE in the repository.

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the LICENSE for the specific language governing permissions and limitations under the License.

This project follows the REUSE standard for software licensing. Each file contains copyright and license information, and license texts can be found in the LICENSES folder. For more information visit https://reuse.software.