What does not belong to supervised learning?

The correct answer is D. Princi1pal component analysis. Principal Component Analysis (PCA) is an unsupervised learning technique - it finds structure in data by reducing dimensionality without using any labeled outputs to guide the process. Logistic regression (A) is a supervised classification algorithm that learns a mapping from…

Machine Learning Basics

Question

Options

ALogistic regression
BSupport vector machine
CDecision tree
DPrinci1pal component analysis

How the community answered

(55 responses)

A
9% (5)
B
5% (3)
C
4% (2)
D
82% (45)

Explanation

Principal Component Analysis (PCA) is an unsupervised learning technique - it finds structure in data by reducing dimensionality without using any labeled outputs to guide the process. Logistic regression (A) is a supervised classification algorithm that learns a mapping from input features to discrete class labels. Support vector machines (B) are supervised models that learn a decision boundary using labeled training examples. Decision trees (C) are supervised learners that partition data based on labeled target values at each split.

Memory tip: Supervised learning always needs a labeled "answer key" to train on. PCA ignores labels entirely - it only looks at the input features themselves - so it cannot be supervised.

Topics

#Supervised Learning#Unsupervised Learning#PCA#Classification

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