Core Prediction Models

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

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1. Which explanation matches linear regression as a useful baseline?A. The sigmoid transformation connects the linear score to the bounded output.B. With one feature, the coefficient describes the model's predicted change in the target for a one-unit feature increase.C. The meaning of close depends on the features, scaling, and distance function.D. Training chooses splits that improve a criterion, such as reducing class impurity or squared error.

2. Which explanation matches logistic regression and decision thresholds?A. The sigmoid transformation connects the linear score to the bounded output.B. With one feature, the coefficient describes the model's predicted change in the target for a one-unit feature increase.C. The meaning of close depends on the features, scaling, and distance function.D. Training chooses splits that improve a criterion, such as reducing class impurity or squared error.

3. Which explanation matches nearest neighbors: learn from similar examples?A. With one feature, the coefficient describes the model's predicted change in the target for a one-unit feature increase.B. The sigmoid transformation connects the linear score to the bounded output.C. Training chooses splits that improve a criterion, such as reducing class impurity or squared error.D. The meaning of close depends on the features, scaling, and distance function.

4. If alerts are cheap but missed failures are very costly, which direction might you explore when adjusting the threshold?A. The prediction is 26 dollars. At 5,000 kilometers the model is extrapolating far beyond an ordinary local-delivery range, where the relationship may change.B. Dollar differences can be numerically much larger and dominate the distance, even when age is equally important to the task.C. Explore lower thresholds to improve sensitivity, while measuring the extra false alarms and confirming the tradeoff on validation data.D. It can fit training values extremely closely while producing unstable predictions for new examples.

5. What can happen when a regression tree has one training example in nearly every leaf?A. The prediction is 26 dollars. At 5,000 kilometers the model is extrapolating far beyond an ordinary local-delivery range, where the relationship may change.B. It can fit training values extremely closely while producing unstable predictions for new examples.C. Explore lower thresholds to improve sensitivity, while measuring the extra false alarms and confirming the tradeoff on validation data.D. Dollar differences can be numerically much larger and dominate the distance, even when age is equally important to the task.

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Core Prediction Models

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