A library consisting of useful tools and extensions for the day-to-day data science tasks.
Sebastian Raschka 2014-2015
Current version: 0.2.6
- 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
## Examples
from mlxtend.evaluate import plot_decision_regions
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.svm import SVC
# Loading some example data
iris = datasets.load_iris()
X = iris.data[:, [0,2]]
y = iris.target
# Training a classifier
svm = SVC(C=0.5, kernel='linear')
svm.fit(X,y)
# Plotting decision regions
plot_decision_regions(X, y, clf=svm, res=0.02, legend=2)
# Adding axes annotations
plt.xlabel('sepal length [cm]')
plt.ylabel('petal length [cm]')
plt.title('SVM on Iris')
plt.show()</pre>
You can use the following command to install mlxtend:
pip install mlxtend
or
easy_install 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