Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
smooth acceleration
B
consistent braking
C
high fuel efficiency
D
comfortable seating
Understanding the Answer
Let's break down why this is correct
Answer
Sensitivity of predictors means the model reacts strongly to small changes in input, which helps keep predictions steady and reliable. In the same way, a car’s responsiveness—how quickly it reacts to steering, throttle, and brakes—directly affects how agile and controllable it feels. When a car is highly responsive, the driver experiences smooth, predictable handling and can anticipate the vehicle’s behavior. Thus, responsiveness of a car is to agility and driver confidence as sensitivity of predictors is to stability in predictions. For example, a sports car that instantly reacts to a turn will feel more nimble and easier to control than a sluggish one.
Detailed Explanation
The car’s ability to change speed quickly when the driver presses the gas pedal shows how input affects output. Other options are incorrect because People may think steady braking is a sign of sensitivity, but it is about consistency, not how fast the car reacts; Fuel efficiency measures how much fuel the car uses, not how it reacts to driver input.
Key Concepts
Sensitivity of Predictors
Responsiveness in Systems
Stability in Predictions
Topic
Sensitivity of Predictors
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In a statistical model, what does the threshold value represent in relation to the sensitivity of predictors?
easyComputer-science
Practice
2
Question 2In the context of predictive modeling, how does the sensitivity of a predictor relate to its specificity?
mediumComputer-science
Practice
3
Question 3In the context of sensitivity analysis, how do confounding variables potentially impact the interpretation of predictor sensitivity in a regression model?
mediumComputer-science
Practice
4
Question 4In predictive modeling, which of the following best describes the relationship between specificity and statistical significance when evaluating the sensitivity of predictors?
hardComputer-science
Practice
5
Question 5The sensitivity of a predictor is essential for ensuring that it can generalize well to new data. It measures how responsive a predictor is to changes in input features, indicating that a highly sensitive predictor will show significant changes in output with small changes in input, while an insensitive predictor will remain relatively stable. Therefore, in the context of this discussion, we could say that sensitivity is crucial for __________.
mediumComputer-science
Practice
6
Question 6Which of the following statements about the sensitivity of predictors are true? Select all that apply.
hardComputer-science
Practice
7
Question 7A new predictor model is being developed. If the model shows significant changes in its predictions with slight variations in the input data, how would you classify its sensitivity?
easyComputer-science
Practice
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