Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks.

Sebastian Raschka 2014-2018

Links

• Documentation: http://rasbt.github.io/mlxtend
• PyPI: https://pypi.python.org/pypi/mlxtend
• Changelog: http://rasbt.github.io/mlxtend/changelog
• Contributing: http://rasbt.github.io/mlxtend/contributing
• Questions? Check out the Google Groups mailing list

Installing mlxtend

PyPI

To install mlxtend, just execute

pip install mlxtend  

Alternatively, you could download the package manually from the Python Package Index https://pypi.python.org/pypi/mlxtend, unzip it, navigate into the package, and use the command:

python setup.py install

Conda

If you use conda, to install mlxtend just execute

conda install -c conda-forge mlxtend 

Dev Version

The mlxtend version on PyPI may always be one step behind; you can install the latest development version from the GitHub repository by executing

pip install git+git://github.com/rasbt/mlxtend.git#egg=mlxtend

Or, you can fork the GitHub repository from https://github.com/rasbt/mlxtend and install mlxtend from your local drive via

python setup.py install

Examples

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import itertools
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from mlxtend.classifier import EnsembleVoteClassifier
from mlxtend.data import iris_data
from mlxtend.plotting import plot_decision_regions

# Initializing Classifiers
clf1 = LogisticRegression(random_state=0)
clf2 = RandomForestClassifier(random_state=0)
clf3 = SVC(random_state=0, probability=True)
eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], weights=[2, 1, 1], voting='soft')

# Loading some example data
X, y = iris_data()
X = X[:,[0, 2]]

# Plotting Decision Regions
gs = gridspec.GridSpec(2, 2)
fig = plt.figure(figsize=(10, 8))

for clf, lab, grd in zip([clf1, clf2, clf3, eclf],
                         ['Logistic Regression', 'Random Forest', 'RBF kernel SVM', 'Ensemble'],
                         itertools.product([0, 1], repeat=2)):
    clf.fit(X, y)
    ax = plt.subplot(gs[grd[0], grd[1]])
    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)
    plt.title(lab)
plt.show()

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If you use mlxtend as part of your workflow in a scientific publication, please consider citing the mlxtend repository with the following DOI:

@misc{raschkas_2016_49235,
  author       = {Raschka, Sebastian},
  title        = {Mlxtend},
  month        = apr,
  year         = 2016,
  doi          = {10.5281/zenodo.49235},
  url          = {http://dx.doi.org/10.5281/zenodo.49235}
}

---

License

• This project is released under a permissive new BSD open source license (LICENSE-BSD3.txt) and commercially usable. There is no warranty; not even for merchantability or fitness for a particular purpose.
• In addition, you may use, copy, modify and redistribute all artistic creative works (figures and images) included in this distribution under the directory according to the terms and conditions of the Creative Commons Attribution 4.0 International License. See the file LICENSE-CC-BY.txt for details. (Computer-generated graphics such as the plots produced by matplotlib fall under the BSD license mentioned above).

Contact

I received a lot of feedback and questions about mlxtend recently, and I thought that it would be worthwhile to set up a public communication channel. Before you write an email with a question about mlxtend, please consider posting it here since it can also be useful to others! Please join the Google Groups Mailing List!

If Google Groups is not for you, please feel free to write me an email or consider filing an issue on GitHub's issue tracker for new feature requests or bug reports. In addition, I setup a Gitter channel for live discussions.