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VisualML: Visualization of Multi-Dimensional Machine Learning Models

Project description

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)

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