Classification where data is assigned exclusively to one of three or more options (e.g., categorizing page type: blog post, FAQ page, landing page).
Multi-Class Classification is a type of single-label classification where the model predicts one outcome from three or more options. The key feature is that the predicted outcome is exclusive; the data point can only be assigned to a single class label.
This classification is appropriate for categorizing content into non-overlapping groups. An SEO example is determining what type of content a page is, classifying it as either a Blog, Product, FAQ, or Other. Algorithms such as Naive Bayes and Support Vector Machine (SVM) can be used for this task.
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Explore other Task Types terms
B
Binary Classification
Classification task with two possible outcomes (e.g., positive or negative sentiment).
C
Centroid-based Clustering
Organizes data into non-hierarchical clusters based on the arithmetic mean (centroid) of the points. Efficient…
C
Clustering (ML Task)
Grouping data points into clusters based on similarity; an unsupervised learning task.
D
Density-based Clustering
Groups data points based on density and proximity. Does not require pre-defining the number of…
D
Distribution-based Clustering
Assumes data is composed of probabilistic distributions (e.g., Gaussian Mixture Model).
H
Hard Clustering
A type of clustering where data points are assigned exclusively to a single cluster.
H
Hierarchical Clustering
A clustering approach where data points are recursively merged or split to create a tree-like…
M
Multi-Label Classification
Classification where an input can belong to multiple categories simultaneously (e.g., tagging a blog post…
S
Soft/Fuzzy Clustering
A type of clustering where data points can belong to multiple topics/clusters with varying probabilities…