Instance-Based Learning

Five questions. Choose one answer for each, then submit to see your score and explanations.

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1. Which explanation matches define a distance that makes sense?A. Fit scaling parameters on training data, then apply them unchanged to later examples.B. Neighbor count controls how local or smooth a nearest-neighbor prediction becomes.C. A distance measure encodes which features count as similar and at what scale.D. In high dimensions, distances can become less informative and data can be sparse.

2. Which explanation matches scale features before nearest neighbors?A. A distance measure encodes which features count as similar and at what scale.B. Fit scaling parameters on training data, then apply them unchanged to later examples.C. Neighbor count controls how local or smooth a nearest-neighbor prediction becomes.D. In high dimensions, distances can become less informative and data can be sparse.

3. Which explanation matches tune neighbor count on validation data?A. Neighbor count controls how local or smooth a nearest-neighbor prediction becomes.B. A distance measure encodes which features count as similar and at what scale.C. Fit scaling parameters on training data, then apply them unchanged to later examples.D. In high dimensions, distances can become less informative and data can be sparse.

4. For scale features before nearest neighbors, which result or action fits this practical case: Mileage dominates an unscaled neighbor distance over a small weather score.A. Compare raw mileage distance with a representation including weather and route type.B. Compare one, five, and twenty neighbors on the same validation period.C. Compare validation results before and after removing redundant features.D. Standardize distance and temperature before comparing nearby trips.

5. For recognize high-dimensional distance problems, which result or action fits this practical case: The dataset adds dozens of weakly relevant delivery features.A. Compare raw mileage distance with a representation including weather and route type.B. Standardize distance and temperature before comparing nearby trips.C. Compare validation results before and after removing redundant features.D. Compare one, five, and twenty neighbors on the same validation period.

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Instance-Based Learning

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