Lab 8: Classification

Not graded, just practice

Classification

1. Which of the following aspects of model building apply to classification models? (Choose all that apply)

specify fit accuracy reliability

2. What is the difference between regression and classification?

"Regression predicts a continuous response varaible, 
classification predicts a discrete response variable"

3. Which accuracy metric is best applied to classification models?

R squared RMSE Pecent correct All of the above

4. In the figure below, which aspect shows the response variable?

x-axis y-axis color of points blue line

5. Which figure below could show a plotted classification model? Choose all that apply.

Figure A Figure B Figure C

6. Name two kinds of linear classifiers

"Any 2 of those mentioned in class: 
Logistic regression
Linear discriminant analysis (LDA)
Linear support vector machines (SVM)
Nearest-prototype classifiers
Naive Bayes classifiers
"

4 Classification in R

1. We can impliment classification via linear regressioncubic polynomialslogistic regressionnearest-neighbor regressionall of the above

2. True or false, in R, we can perform logistic regression with a generalized linear model.

True False

3. What 3 elements do all GLMs have?

"1. A particular distribution for modeling the response variable
2. A linear model 
3. A link function
"

4. What is the link function for logistic regression?

logistic function polynomial expansion log transformation inverse transformation

6. Which of the following fits a logistic regression model in R? Choose one.

# code A
glm(y ~ x, data = data, family = "binomial")

# code B
data %>%
    specify(y ~ x) %>%
    fit() 

# code C 
logistic_reg %>%
    set_engine("glm") %>%
    fit(y ~ x, data = data)

code A code B code C code A and B only all of the above