Metadata-Version: 2.0
Name: visualml
Version: 0.1b4
Summary: VisualML: Visualization of Multi-Dimensional Machine Learning Models
Home-page: https://github.com/wittmannf/visual-ml/
Author: Fernando Marcos Wittmann
Author-email: fernando.wittmann@gmail.com
License: BSD-4-Clause
Keywords: visualml
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6

Visual ML is a library for visualizing the decision boundary of 
machine learning models from Sklearn using 2D projections of pairs
of features. Here's an example:
```
>>> import visualml as vml
>>> import pandas as pd
>>> from sklearn.datasets import make_classification
>>> from sklearn.ensemble import RandomForestClassifier as RF

>>> # Create a toy classification dataset
>>> feature_names = ['A','B','C','D']
>>> X, y = make_classification(n_features=4, random_state=42)

>>> # The visualization is only supported if X is a pandas df
>>> X = pd.DataFrame(X, columns=feature_names)

>>> # Train a classifier
>>> clf = RF(random_state=42).fit(X,y) 

>>> # Plot decision boundary grid
>>> vml.decision_boundary_grid(clf, X, y)
```



