Learn to distinguish true clustering from other techniques. We explain key examples like document grouping, patient segmentation, and allergy severity prediction.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
- Organizing documents into groups based on similarities of the text contained in the documents is an example of clustering.
- Grouping similar patients based on symptoms and diagnostic test results is an example of clustering.
- Predicting whether a person will develop mild, moderate, or severe allergy symptoms based on pollen count is an example of clustering.
Clustering is a machine learning task that is used to group instances of data into clusters that contain similar characteristics. Clustering can also be used to identify relationships in a dataset
Regression is a machine learning task that is used to predict the value of the label from a set of related features.
Statement 1: Yes, organizing documents into groups based on similarities of the text contained in the documents is an example of clustering. Clustering algorithms can group documents together that contain similar words, phrases, or topics.
Statement 2: Yes, grouping similar patients based on symptoms and diagnostic test results is an example of clustering. Medical datasets can be clustered to find patients with similar characteristics and conditions.
Statement 3: No, predicting allergy severity based on pollen count is not clustering. While pollen data could be used in a clustering algorithm, predicting a single severity level is a classification task rather than clustering different groups.
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