A library of extension and helper modules for Python's data analysis and machine learning libraries.
Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks.
Sebastian Raschka 2014-2024
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
If you use conda, to install mlxtend just execute
conda install -c conda-forge mlxtend
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
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()
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 = {https://joss.theoj.org/papers/10.21105/joss.00638}
}
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.