Classification task with two possible outcomes (e.g., positive or negative sentiment).
Binary Classification is a type of single-label classification where the model predicts precisely one of two possible outcomes. It is a supervised learning task where the target variable is discrete.
Common applications include questions with a simple Yes/No answer. An SEO example application cited is predicting whether a website is mobile-friendly (Yes/No). The Logistic Regression algorithm is a classification algorithm commonly used for binary classification tasks.
Sources & References
Explore other Task Types terms
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-Class Classification
Classification where data is assigned exclusively to one of three or more options (e.g., categorizing…
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…