A library consisting of useful tools and extensions for the day-to-day data science tasks.
- This open source project is released under a permissive new BSD open source license and commercially usable
Sebastian Raschka 2014-2016
- Documentation: http://rasbt.github.io/mlxtend/
- Source code repository: https://github.com/rasbt/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
## Recent changes
- Sequential Feature Selection algorithms: SFS, SFFS, and SFBS
- Neural Network / Multilayer Perceptron classifier
- Ordinary least square regression using different solvers (gradient and stochastic gradient descent, and the closed form solution)
To install mlxtend, just execute
pip install mlxtend
The mlxtend version on PyPI may always one step behind; you can install the latest development version from this GitHub repository by executing
pip install git+git://github.com/rasbt/mlxtend.git#egg=mlxtend
Alternatively, you 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
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.evaluate 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', 'Naive Bayes', '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()