Practice Questions
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In the context of parametrized predictors, which estimation technique is commonly used to determine the parameters of a regression model?
MLE picks the parameter values that make the observed data most likely. Other options are incorrect because Simple random sampling is about choosing a...
In a logistic regression model, which of the following best describes the role of a parametrized predictor?
A parametrized predictor is an input that helps the model estimate how likely a binary outcome is. Other options are incorrect because This option mix...
In a linear regression model, how does the goodness-of-fit measure relate to the interpretation of parameters?
Goodness‑of‑fit tells us how well the model explains the data. Other options are incorrect because People think a high R‑squared means the parameters ...
In the context of parametrized predictors, which combination of estimation techniques and regularization methods can lead to improved model evaluation by reducing overfitting?
Elastic Net mixes L1 and L2 penalties, so it shrinks coefficients and drops some variables while keeping others. Other options are incorrect because P...
In the context of logistic regression, which estimation technique is primarily used to maximize predictive accuracy while ensuring that the model effectively predicts binary outcomes?
Maximum Likelihood Estimation, or MLE, looks for the parameter values that make the observed data most probable. Other options are incorrect because O...
A linear regression model is able to predict the output accurately for a given set of inputs. What underlying factor is primarily responsible for the model's performance in terms of prediction accuracy?
The model’s structure, which is defined by how its parameters are arranged, decides what patterns it can capture. Other options are incorrect because ...
Linear regression:Parameters :: Neural networks:?
In a neural network, the numbers that decide how much one neuron influences another are called weights. Other options are incorrect because Activation...
Which of the following scenarios best exemplifies the use of a parametrized predictor?
A uses a linear regression model. Other options are incorrect because B uses a fixed list of keywords; C groups customers without using any numbers to...
In the context of parametrized predictors, which statement best describes the role of parameters in the predictive model?
Parameters decide how the model behaves. Other options are incorrect because It assumes parameters only give a number, but they actually control the m...
In the context of parametrized predictors, the function g defines the ______ of the predictor, while the parameters determine its specific characteristics and behavior.
The function g tells the model what shape it should have. Other options are incorrect because Output is what the model finally gives you; Data refers ...
A 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?
The model’s accuracy depends on the values of its parameters. Other options are incorrect because Linear regression does not automatically tune its we...
Which of the following statements about parametrized predictors are true? Select all that apply.
Parameters decide how the predictor works, so they set its shape and behavior. Other options are incorrect because Some think predictors only work for...
Arrange the following steps in the correct order for constructing a parametrized predictor model: 1) Define the structure of the predictor, 2) Choose the parameters, 3) Collect the data, 4) Optimize the parameters using empirical risk minimization.
First you need data to see what the model will learn. Other options are incorrect because It assumes you can design and set parameters before seeing a...
In the context of parametrized predictors, which aspect most directly influences the model's capacity to generalize to unseen data?
The structure of the predictor function decides how the parameters are used with the input data. Other options are incorrect because People think the ...
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