Welcome to vfi’s documentation!¶
The vfi package implements a variant of a determnistic machine learning method which is called classification by Voting Feature Intervals 1, VFI in short. VFI is a supervised classification method that constuct intervals around each class for each feature. Class counts are recorded for each interval on each feature and the classification is performed using a voting scheme.
G. Demiroz, A. Guvenir: Classification by voting feature intervals. In: 9th European Conference on Machine Learning, 85-92, 1997.01.
How to use VFI¶
The vfi package inherits from sklearn classes, and thus drops in neatly
next to other sklearn classifiers with an identical calling API. Similarly it
supports input in a variety of formats: an array (or pandas dataframe) of
(num_samples x num_features).
import vfi from sklearn.datasets import load_iris data, target = load_iris(return_X_y=True) model = vfi.VFI() model.fit(data, target)