Which learning paradigm is characterized by training an algorithm on a dataset containing both feature inputs and corresponding ground-truth target labels?
Supervised learning algorithms infer a mapping function from input variables (X) to labeled target outputs (Y) using labeled training data.
Which of the following machine learning tasks are canonical examples of Supervised Learning?
Both regression (continuous prediction) and classification (discrete category prediction) require labeled datasets and belong to supervised learning.
Which of the following machine learning tasks are canonical examples of Unsupervised Learning?
Clustering, dimensionality reduction (PCA), and unlabeled anomaly detection are all canonical examples of unsupervised learning.
Which algorithm is a classic centroid-based partitioning technique in Unsupervised Clustering?
K-Means is an unsupervised clustering algorithm that iteratively partitions n observations into k clusters, assigning each point to the nearest cluster centroid.
What is the primary operational objective of Principal Component Analysis (PCA)?
PCA computes the eigenvectors of the data covariance matrix to transform correlated features into a smaller set of orthogonal linear principal components capturing maximum variance.