Detailed breakdown of key machine learning concepts – regression, classification, and clustering.
Table of Contents
Question
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Statement 1: You train a regression model by using unlabeled data.
Statement 2: The classification technique is used to predict sequential numerical data over time.
Statement 3: Grouping items by their common characteristics is an example of clustering.
Answer
Statement 1: You train a regression model by using unlabeled data: No
Statement 2: The classification technique is used to predict sequential numerical data over time: No
Statement 3: Grouping items by their common characteristics is an example of clustering: Yes
Explanation
Statement 1: No Explanation: Regression models are typically trained using labeled data, where input-output relationships are established. Unlabeled data is often used in unsupervised learning, such as clustering or generative models.
Statement 2: No Explanation: The technique used for predicting sequential numerical data over time is time series forecasting, not classification. Classification predicts categories or classes for input data.
Statement 3: Yes Explanation: Clustering involves grouping items based on similarities in their characteristics, allowing the identification of inherent patterns or structures within data.
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