Multilabel Classification Using Scikit-Learn

Discover how to create a multilabel classifier in your work.

In machine learning, classification is a supervised learning technique that predicts labels based on input data. For instance, we analyze historical features to assess if someone is interested in a sales offering. By training the model with available training data, we can classify new incoming data.

We frequently face standard classification challenges, including binary classification (with two labels) and multiclass classification (with more than two labels).

In this scenario, we would train the classifier, and the model would strive to predict one of the labels from all the provided options. The dataset utilized for classification looks like the image below.

The image above demonstrates that the target (Sales Offering) has two labels in Binary Classification and three in Multiclass Classification. The model will train on the available features and subsequently generate only one label.

Multilabel classification is distinct from binary or multiclass classification. Rather than predicting a single output label, it focuses on assigning all relevant labels to the data. Consequently, the outcome can include anywhere from no labels to the full spectrum of available labels.

Multilabel classification is commonly used in text data classification tasks. For example, here is a sample dataset for multilabel classification.

In the example above, examine Texts 1 to 5, which can be divided into four categories: Event, Sport, Pop Culture, and Nature. Based on the training data provided, the Multilabel Classification task determines which label corresponds to the given sentence. These categories are not mutually exclusive; each label can be viewed as independent.

For more details, we can observe that Text 1 labels Sport and Pop Culture, while Text 2 labels Pop Culture and Nature. This indicates that each label is mutually exclusive, and Multilabel Classification can yield prediction outputs of none of the labels or all of the labels simultaneously.

With that introduction, let’s attempt to build a Multiclass Classifier with Scikit-Learn.