Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
It serves as an independent variable that predicts the probability of a binary outcome.
B
It measures the variability of the dependent variable.
C
It transforms the dependent variable into a categorical variable.
D
It is used to calculate the mean of the response variable.
Understanding the Answer
Let's break down why this is correct
Answer
In logistic regression the parametrized predictor is the linear combination of the input variables that the model uses to compute the log‑odds of the outcome. It takes the form β₀ + β₁x₁ + … + βₖxₖ, where the βs are the parameters to be learned from data. By adjusting these parameters the model can fit the relationship between the predictors and the probability of the event. For example, if β₀ = –2, β₁ = 0. 5 and β₂ = –1, then for a data point with x₁ = 4 and x₂ = 3 the predictor equals –2 + 0.
Detailed Explanation
A parametrized predictor is an input that helps the model estimate how likely a binary outcome is. Other options are incorrect because This option mixes up the predictor with the outcome; Predictors do not turn the outcome into categories.
Key Concepts
statistical modeling
logistic regression
Topic
Parametrized Predictors
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In the context of parametrized predictors, which estimation technique is commonly used to determine the parameters of a regression model?
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Question 2In the context of parametrized predictors, which combination of estimation techniques and regularization methods can lead to improved model evaluation by reducing overfitting?
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3
Question 3Which of the following scenarios best exemplifies the use of a parametrized predictor?
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4
Question 4In the context of parametrized predictors, which statement best describes the role of parameters in the predictive model?
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5
Question 5A startup is developing a predictive model to forecast sales based on various marketing strategies. They decide to use a linear regression model as their parametrized predictor. Which of the following statements best describes a crucial aspect of their model design related to the parametrized nature of the predictor?
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6
Question 6Which of the following statements about parametrized predictors are true? Select all that apply.
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7
Question 7In the context of parametrized predictors, which aspect most directly influences the model's capacity to generalize to unseen data?
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8
Question 8Which of the following best describes the role of loss functions in predictive modeling?
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9
Question 9Which of the following statements about regularizers in predictive models are correct? Select all that apply.
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