what is the difference between multilabel and multilabel-multiclass classification? I am trying to classify news articles into their required category. However I am confused by the above(multilabel and multilabel-multiclass) terminologies. My dataset consists of 2 csv files. The first file contains content of the news article. The second file in that same corresponding row number consists of labels associated with it. Here is the sample entries in second file i.e. labels file:



So for row number 1 the first csv file contains news article which is labelled as sport and football. Similarly for row number 2, the article in first csv has labels sport and media. My question here is, is this dataset multi-label or multilabel-multiclass? what is the difference between them?
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.

Multioutput-multiclass (or multilabel-multiclass) classification means that a single estimator has to handle several joint classification tasks. This is both a generalization of the multi-label classification task, which only considers binary classification, as well as a generalization of the multi-class classification task. The output format is a 2d numpy array or sparse matrix.

As each object of your data can be classified in one to multiple labels at the same time, it is a simple multilabel classification problem

Taken from : https://scikit-learn.org/stable/modules/multiclass.html