logistic regression

Question 1

DV in logistic regression is what type of variable

Answer 1

binary

Question 2

baseline model predicts what

Answer 2

most common occurrence not what value we after

Question 3

categorical variable is from the model which is

Answer 3

small number of possibel outcomes

Question 4

values of logstic vs linear regression

Answer 4

logsitic = between 0 and 1
linear = infinity- negativr infinity

Question 5

if linear regression y = 0 logstic will =

Answer 5

0.5

Question 6

higher thann 0 in linear regression = in logsitic regression

Answer 6

greater than 0.5

Question 7

in the form of a prediction if linear regression y> 0 logstic =

Answer 7

1

Question 8

logsitic is non linear how to make it back to linear regression

Answer 8

use odds

Question 9

although y value is 0 or 1 outcome variable output will be

Answer 9

probabaility between 0 and 1, output is a probaility not 0 or 1 but between them

Question 10

when we build logistic regression model y will take value of 0 or 1 but outcome variable will be

Answer 10

continuos score/ probability between 1 or 0

Question 11

output is

Answer 11

probability not or 1 but between them

Question 12

create model using what set and evaluate on what set

Answer 12

training, testing

Question 13

selecting a threshold of 0.5 predicts

Answer 13

most likley outcome

Question 14

ROC helps us and what are its axis

Answer 14

pick threshold, TP rate or sensitivity on y axis, TN or specificity on x axis

Question 15

ROC captures

Answer 15

all thresholds simultaneously

Question 16

high thresholds have

Answer 16

high speceficty and low sensitivty

Question 17

low thresholds have

Answer 17

low speceficty and high sesnsitivty

Question 18

2 main ways to evaluate the model

Answer 18

AUC and accuracy

Question 19

what are some issues with accuracy, sensitivity, and specificity

Answer 19

depend on the threshold

Question 20

AUC is area under curve what is the interpratation

Answer 20

given a random positive and negative proportion of the time you can guess which is correct.

Question 21

AUC is less effected by what then accuracy

Answer 21

sample balance

Question 22

maximum of AUC

Answer 22

1 perfect prediction: your false positive rate is zero, sensitivity goes up to 1 without compromise on FP rate

Question 23

Minimum AUC

Answer 23

0.5 -> just guessing

Question 24

when deciding which model is best use

Answer 24

accuracy, senssitivy and speceficty. caculate gains and losses from model to the baseline model

Question 25

in the box plot is prediciting

Answer 25

avergae scores for patient not probability