1. All variables selected to build a decision tree model will be used to predict the target variable.

Group of answer choices

True

False

2.Select the correct statement:

Group of answer choices

Decision trees tend to underfit the model to the data.

Decision trees tend to overfit the model to the data.

Decision trees always underfit the model to the data.

Decision trees do not overfit the model to the data.

3.Select the correct statement:

Group of answer choices

Overfitting is when the model performs well on the training data and performs accurately in the evaluation set.

Overfitting is when the model does not perform well on the training data and does not perform accurately in the evaluation set.

Overfitting is when the model performs well on the training data but does not perform accurately in the evaluation set.

Overfitting is when the model does not perform well on the training data but performs accurately in the evaluation set.

4.Select the correct statement:

Group of answer choices

Boosted models consist of multiple decision tree models

Boosted models consist of a single random forest model

Boosted models consist of a single decision model

Boosted models consist of multiple random forest models

5.Select the correct statement:

Group of answer choices

Logistic regression models predict categorical data

Logistic regression models predict both quantitative and categorical data

Logistic regression models predict quantitative data

All of answers are correct

6.Select the correct statement:

Group of answer choices

For a random forest model, the ideal number of trees is the smallest possible number of trees.

For a random forest model, the ideal number of trees is the greater possible number of trees.

None of the answers are correct.

For a random forest model, the ideal number of trees is 10 trees.

7.Select the correct statement:

Group of answer choices

Stepwise regression identifies the best combination of variables.

Akaike information criterion (or AIC) is not the only available method for model selection when performing a stepwise regression.

Stepwise regression is an automated way to identify which variables are important to the model.

All of the answers are correct.

8.To tell if a Forest Model is a sound model we use:

Group of answer choices

All of the answers are correct.

The Percentage Error for Different Number of Trees graph.

Confusion Matrix

Out of the Bag Error Rate

9.When performing a logistic regression we need to look at:

Group of answer choices

The coefficient of determination.

None of the coefficient of determination and the p-values.

The p-values.

Both coefficient of determination and p-values.

10.When the target variable is a quantitative variable, which of the following statements is correct?

Group of answer choices

A classification model can be used after the target variable is transformed into a categorical variable.

We can never use a classification model.

A linear regression model can be used after the target variable is transformed into a categorical variable.

We can only use the linear regression model.