Welcome to mlxtend's documentation!

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

DOI PyPI version Python 3.6 Python 3.7 License Discuss



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))

labels = ['Logistic Regression',
          'Random Forest',
          'RBF kernel SVM',
          'Ensemble']

for clf, lab, grd in zip([clf1, clf2, clf3, eclf],
                         labels,
                         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:

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}
}

License

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.