AI-900: Predicting Race Time with Machine Learning

Learn about predicting race time with machine learning. Understand the difference between regression, clustering, and classification.

Question

Predicting how many minutes it will take someone to run a race based on past race times is a use case for?

A. Clustering
B. Classification
C. Regression

Answer

C. Regression

Explanation

Regression is a supervised machine learning technique used to predict numeric values.

The correct answer is C. Regression.

Regression is a type of supervised machine learning that predicts a continuous numerical value based on the input features. For example, predicting how many minutes it will take someone to run a race based on past race times is a use case for regression, because the output is a numerical value that can vary within a range. Regression models learn from the historical data and try to find the best fit line or curve that minimizes the error between the actual and predicted values.

Clustering is a type of unsupervised machine learning that groups similar data points together based on their features. For example, clustering customers based on their purchase behavior is a use case for clustering, because the output is a set of discrete groups or clusters that are not predefined. Clustering models do not use any labels or targets, but instead rely on the inherent structure or patterns in the data.

Classification is another type of supervised machine learning that predicts a discrete categorical value based on the input features. For example, predicting whether an email is spam or not based on its content is a use case for classification, because the output is a binary value that can only be either spam or not spam. Classification models learn from the labeled data and try to find the best decision boundary that separates the different classes.

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