Question 6 (1 point)

Logistic Regression models:

Question 6 options:

	The probability of outcome of the dependent variable
	The average of the dependent variable
	The log of the dependent variable
	The square root of the dependent variable

Question 7 (1 point)

In logistic regression, the logistic function is used to:

Question 7 options:

	Transform the variable using a log function
	Produce a probability value
	Produce a value below 0 and above 1
	Produce a bounded value between -1 and +1

Question 8 (1 point)

What is the commonly used estimator employed to estimate the parameters in logistic regression?

Question 8 options:

	No estimator is used, there exists a closed-form solution
	Mean-Squared Error (MSE) Estimator
	Least-Square Estimator
	Maximum Likelihood Estimator (MLE)

Question 9 (1 point)

Extending logistic regression to predict variables with multiple categories is called

Question 9 options:

	Multiple logistic regression
	Binomial logistic regression
	Multinomial logistic regression
	Binary Logistic regression

Question 10 (1 point)

Logistic regression is a specific type of a generalized linear model. What is the appropriate distribution type for the dependent variable in logistic regression when used as a genialized linear model?

Question 10 options:

	Binomial
	Poisson
	Gaussian
	Gamma

Question 11 (1 point)

Which one describes best about model validation and model testing?

Question 11 options:

	Once the validation step is done, the validation set is combined with the test set to assess the overall performance of the model
	Both model validation and testing are ways to measure the model performance, only one is needed.
	If the training data is small, there is no need to have an independent test set, the valuation step can be used to evaluate the model performance
	Validation is used to help tune the model using a validation set while testing is used to evaluate the model performance using an independent set

Question 12 (1 point)

What are acceptable ways to evaluate a classification model performance? Select all that apply.

Question 12 options:

	Use training set
	Use cross validation
	Use out of sample
	Use independent test set

Question 13 (1 point)

A significant reduction in the model accuracy using a test set compared to a training set is an indication of?

Question 13 options:

	Non-linearity
	Underfitting
	Complexity
	Overfitting

Question 14 (1 point)

When does a simple accuracy do a reasonably good job in assessing the performance of a classification model?

Question 14 options:

	For imbalanced data
	For normally distributed data
	For data with many categories
	For a balanced data

Question 15 (1 point)

Which is more important for a classification model, precision or recall?

Question 15 options:

	The both are equally important and should be weighted proportionally
	Recall as it evaluate the detection capability of the model
	It depends on the application; in some, precision is more important than recall and vise versa
	Precision as it measures the true classification capability of the model