MLXtend: Easily Extend and Enhance Python's Machine Learning Libraries

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

Sebastian Raschka 2014-2023

Installing mlxtend


To install mlxtend, just execute

pip install mlxtend  

Alternatively, you could download the package manually from the Python Package Index, unzip it, navigate into the package, and use the command:

python install


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://

Or, you can fork the GitHub repository from and install mlxtend from your local drive via

python install


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 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)):, y)
    ax = plt.subplot(gs[grd[0], grd[1]])
    fig = plot_decision_regions(X=X, y=y, clf=clf, legend=2)

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

  author       = {Sebastian Raschka},
  title        = {MLxtend: Providing machine learning and data science 
                  utilities and extensions to Python’s  
                  scientific computing stack},
  journal      = {The Journal of Open Source Software},
  volume       = {3},
  number       = {24},
  month        = apr,
  year         = 2018,
  publisher    = {The Open Journal},
  doi          = {10.21105/joss.00638},
  url          = {}
  • Raschka, Sebastian (2018) MLxtend: Providing machine learning and data science utilities and extensions to Python's scientific computing stack. J Open Source Softw 3(24).


The best way to ask questions is via the GitHub Discussions channel. In case you encounter usage bugs, please don't hesitate to use the GitHub's issue tracker directly.


Download Details:

Author: rasbt
Source Code: 
License: View license

#machinelearning #python #datascience #datamining 

MLXtend: Easily Extend and Enhance Python's Machine Learning Libraries
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