Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
It ensures predictions are stable with small input changes.
B
It increases the complexity of the model.
C
It reduces the amount of training data needed.
D
It guarantees high accuracy in all scenarios.
Understanding the Answer
Let's break down why this is correct
Answer
Sensitivity of predictors tells us how much a model’s output changes when a predictor changes. If a predictor is highly sensitive, a small error or noise in that predictor can produce a big change in the prediction, which can hurt the model’s reliability. Knowing which predictors are sensitive helps us prioritize cleaning data, adding regularization, or choosing more stable features. For example, if a credit‑score model is very sensitive to a borrower’s income, a slight misreport can flip the decision from loan approved to denied, so we would double‑check that income data. By focusing on sensitive predictors, we make the model more robust and trustworthy.
Detailed Explanation
Sensitivity tells how much a prediction changes when a feature changes a little. Other options are incorrect because People think that making a model more sensitive means it is more complex; Some believe that a sensitive predictor means you need less data.
Key Concepts
Sensitivity of Predictors
Generalization in Machine Learning
Model Performance
Topic
Sensitivity of Predictors
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In the context of predictive modeling, how does the sensitivity of a predictor relate to its specificity?
mediumComputer-science
Practice
2
Question 2In the context of sensitivity analysis, how do confounding variables potentially impact the interpretation of predictor sensitivity in a regression model?
mediumComputer-science
Practice
3
Question 3In predictive modeling, which of the following best describes the relationship between specificity and statistical significance when evaluating the sensitivity of predictors?
hardComputer-science
Practice
4
Question 4How does the sensitivity of a predictor impact its generalization ability in machine learning?
easyComputer-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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