Explore the necessity of numeric labels in regression and classification models, unraveling the role of labeling in effective AI model training and accuracy.
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: For a regression model, labels must be numeric.
Statement 2: For a clustering model, labels must be used.
Statement 3: For a classification model, labels must be numeric.
Statement 1: For a regression model, labels must be numeric: Yes
Statement 2: For a clustering model, labels must be used: No
Statement 3: For a classification model, labels must be numeric: No
Box 1: Yes -For regression problems, the label column must contain numeric data that represents the response variable. Ideally the numeric data represents a continuous scale.
Box 2: No -K-Means Clustering -Because the K-means algorithm is an unsupervised learning method, a label column is optional. If your data includes a label, you can use the label values to guide selection of the clusters and optimize the model. If your data has no label, the algorithm creates clusters representing possible categories, based solely on the data.
Box 3: No -For classification problems, the label column must contain either categorical values or discrete values. Some examples might be a yes/no rating, a disease classification code or name, or an income group. If you pick a noncategorical column, the component will return an error during training.
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