Explore the powerful K-means algorithm, a cornerstone in Clustering Models, understanding its role in segmenting and grouping data for robust machine learning solutions.
Table of Contents
Question
When you are creating a Clustering Model, what common ML algorithm are you using?
A. Multicast Logistic Regression
B. K-means
C. Linear Regression
D. Two-Class Neural Network
E. Decision Forest Regression
Answer
B. K-means
Explanation
When creating a Clustering Model, the common ML algorithm used is “B. K-means.” It’s specifically designed to segment and group data points into clusters based on similarities.
The Clustering is a Machine Learning form that groups items based on some common properties.
The most common Clustering algorithm is K-means Clustering.
Option A is incorrect because the Multicast Logistic Regression is a Classification algorithm based on a decision forest algorithm.
Option C is incorrect because the Linear Regression algorithm is a Regression algorithm based on a linear regression model.
Option D is incorrect because the Two-Class Neural Network is a Classification algorithm based on a neural network algorithm.
Option E is incorrect because the Decision Forest Regression algorithm is a Regression algorithm based on a decision forest algorithm.
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