A Python library of useful tools for the day-to-day data science tasks

Mlxtend

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

Sebastian Raschka 2014-2021

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()

ensemble_decision_regions_2d

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

@article{raschkas_2018_mlxtend,
  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          = {http://joss.theoj.org/papers/10.21105/joss.00638}
}

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).