Discuss@GL4L

Multiclass and multilabel algorithms


#1

The sklearn.multiclass module implements meta-estimators to solve multiclass and multilabel classification problems by decomposing such problems into binary classification problems. Multitarget regression is also supported.

Multiclass classification means a classification task with more than two classes; e.g., classify a set of images of fruits which may be oranges, apples, or pears. Multiclass classification makes the assumption that each sample is assigned to one and only one label: a fruit can be either an apple or a pear but not both at the same time.

Multilabel classification assigns to each sample a set of target labels. This can be thought as predicting properties of a data-point that are not mutually exclusive, such as topics that are relevant for a document. A text might be about any of religion, politics, finance or education at the same time or none of these.

Take a look at the below link for the whole package explanation with example:

http://scikit-learn.org/stable/modules/multiclass.html

Explore more. Happy Learning!