Multi-label classification in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is multi-label classification?

Multi-label classification is a type of machine learning task where each example can belong to more than one class or category at the same time. For example, a photo can have both 'cat' and 'dog' labels.

beginner

How does multi-label classification differ from multi-class classification?

In multi-class classification, each example belongs to only one class out of many. In multi-label classification, each example can belong to multiple classes simultaneously.

intermediate

Name a common method to handle multi-label classification.

One common method is to use a separate binary classifier for each label. This means the model predicts yes/no for each label independently.

intermediate

What metric can be used to evaluate multi-label classification models?

Metrics like Hamming Loss, F1-score (micro and macro), and Jaccard Index are used to evaluate multi-label classification models because they consider multiple labels per example.

beginner

Why is multi-label classification important in real life?

Because many real-world problems involve items that belong to multiple categories, like tagging photos, music genres, or medical diagnoses, multi-label classification helps models understand and predict these complex cases.

In multi-label classification, an example can have:

A Multiple labels at the same time

B Only one label

C No labels

D Labels that are mutually exclusive

Multi-label classification allows each example to have multiple labels simultaneously.

Which metric is suitable for evaluating multi-label classification?

A Accuracy for single label

B Mean Squared Error

C Hamming Loss

D BLEU score

Hamming Loss measures how many labels are incorrectly predicted in multi-label classification.

A simple way to build a multi-label classifier is to:

A Train one multi-class classifier

B Train one binary classifier per label

C Use clustering algorithms

D Ignore label dependencies

Training one binary classifier per label is a common approach for multi-label classification.

Which of these is NOT true about multi-label classification?

A Labels are mutually exclusive

B Each example can belong to multiple classes

C It is used in tagging images

D It requires special evaluation metrics

In multi-label classification, labels are not mutually exclusive; examples can have multiple labels.

Multi-label classification is useful when:

A There are no categories

B Items belong to exactly one category

C Categories are hierarchical only

D Items can belong to several categories

Multi-label classification handles cases where items belong to multiple categories at once.

Explain what multi-label classification is and how it differs from multi-class classification.

Think about how many labels an example can have.

Describe one common method to build a multi-label classification model and name a metric to evaluate it.

Consider how to predict each label separately.