How does the shareboost algorithm works in shogun?
This recipe explains how does the shareboost algorithm works in shogun.
Recipe Objective
This recipe explains how does the shareboost algorithm works in shogun.
For more related projects-
Project 1
Project 2
ShareBoost
The shareboost algorithm uses a forward greedy selection approach and learns a multiclass predictor from a subset of shared features of the samples
x_train = RealFeatures(feats_train)
x_test = RealFeatures(feats_test)
y_train = MulticlassLabels(labels_train)
y_test = MulticlassLabels(labels_test)
s = ShareBoost(x_train, y_train, 2)
s.train()
z = RealSubsetFeatures(x_test, s.get_activeset())
predict = shareboost.apply_multiclass(z)
eval = MulticlassAccuracy()
accuracy = eval.evaluate(predict, y_test)
What Users are saying..
Gautam Vermani
Having worked in the field of Data Science, I wanted to explore how I can implement projects in other domains, So I thought of connecting with ProjectPro. A project that helped me absorb this topic... Read More