The k-nearest neighbor (k-NN) technique identifies the k observations in the training data that are most similar (or nearest) to a new observation we want to classify.
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Q14: The Mahalanobis distance measure accounts for differences
Q15: If using the regression tool for two-group
Q16: Exhibit 10.1
The following questions are based on
Q17: In hierarchical clustering, the measure of similarity
Q18: The Get Data command is part of
Q20: When purity is perfect, the Gini index
Q21: In discriminant analysis the averages for the
Q22: One element in cleaning the data set
Q23: Discriminant analysis (DA) differs from most other
Q24: Exhibit 10.1
The following questions are based on
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