What is the role of the sigmoid function in logistic regression?

It converts the output into a discrete class label directly

It reduces the dimensionality of the feature space

It maps the linear combination of inputs to a probability value between 0 and 1

It transforms the target variable to a normal distribution

A data scientist is building a linear regression model to predict house prices from 40 correlated features. The model shows high variance on the validation set. Which approach should the data scientist take to reduce overfitting while keeping all features in the model?

Use polynomial regression with degree 3

Use ridge regression with a tuned alpha

Use ordinary least squares with more training data

Use lasso regression with a tuned alpha

GIAC Machine Learning Engineer

Domain 2Objective 2

Regressions GMLE Practice Questions (Page 7)

Part of the Machine Learning Algorithms domain, which makes up ~29% of our current practice bank. GIAC (SANS) does not publish an official question count, but from its 180-minute exam (~70–120 total, ~20–35 in this domain), expect 7–12 from this objective — we provide 36 practice questions to prepare you well beyond it. (estimate)

36questions here

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Questions 31–35

31application · easy
A bank wants to predict whether a loan applicant will default. The target is binary (default or not). The dataset has 10,000 records and 20 features. The model must output a probability of default. Which model should the data scientist use?
Select an answer first
32foundation · easy
Which regularization technique combines both L1 and L2 penalties in its loss function?
Select an answer first
33foundation · easy
What is the role of the sigmoid function in logistic regression?
Select an answer first
34application · medium
A data scientist is building a linear regression model to predict house prices from 40 correlated features. The model shows high variance on the validation set. Which approach should the data scientist take to reduce overfitting while keeping all features in the model?
Select an answer first
35application · medium
A logistic regression model is trained to predict customer churn. The model's coefficients are: intercept = -1.5, coefficient for monthly charge = 0.03. For a customer with a monthly charge of $80, what is the log-odds of churn?
Select an answer first

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Regressions GMLE Practice Questions (Page 7)

Questions 31–35

A bank wants to predict whether a loan applicant will default. The target is binary (default or not). The dataset has 10,000 records and 20 features. The model must output a probability of default. Which model should the data scientist use?

Which regularization technique combines both L1 and L2 penalties in its loss function?

What is the role of the sigmoid function in logistic regression?

A data scientist is building a linear regression model to predict house prices from 40 correlated features. The model shows high variance on the validation set. Which approach should the data scientist take to reduce overfitting while keeping all features in the model?

A logistic regression model is trained to predict customer churn. The model's coefficients are: intercept = -1.5, coefficient for monthly charge = 0.03. For a customer with a monthly charge of $80, what is the log-odds of churn?

Keep studying — Machine Learning Algorithms