Welcome to dagging’s documentation!

The dagging package implements an ensemble method that is called Dagging 1. Dagging creates a number of disjoint, stratified folds out of the data and feeds each chunk of data to a copy of the supplied base classifier. Predictions are made using the average of class membership probabilities if the base estimator outputs probabilities otherwise via plurality vote.


Ting, K. M., Witten, I. H.: Stacking Bagged and Dagged Models. In: Fourteenth international Conference on Machine Learning, San Francisco, CA, 367-375, 1997.

How to use dagging

The dagging 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 shape (num_samples x num_features).

import dagging
from sklearn.datasets import load_iris

data, target = load_iris(return_X_y=True)

model = dagging.DaggingClassifier(random_state=0)
model.fit(data, target)