Midterm 2.... Flashcards Preview

MGMT3200 Business Analytics > Midterm 2.... > Flashcards

Flashcards in Midterm 2.... Deck (46)
Loading flashcards...
1

Convert odds of 1:8 to a probability.

1/8 = .125 --> .125/(.125 + 1) = 11.11% probability. Because 1:8 means 1 out of every 9.

2

Odds EQ for Probability(for/against)

odds(for/against)/odds(for/against) + 1

3

What does a logistic regression model predict?

LogOdds! This will have a range of (-infinity, infinity)

4

How do you convert logOdds to odds?

e^logOdds = odds

5

How do you convert logOdds to probability?

e^logOdds/(e^logOdds + 1)

6

What does logOdds equal in terms of ln(x)?

ln(odds) = logOdds or log(odds) = logOdds

7

What is the range of odds (what are they bound by?)

[0, infinity)

8

What is the range of logOdds (what are they bound by?)

(-infinity, infinity)

9

What type of estimation model is logistic regression, and why?

Class probability estimation model. It is using a numeric value to estimate the probability of a categorical variable! Ex. What is the chance Marc goes to class? 0.3

10

What loss function does support vector machine use?

Hinge loss

11

Hinge loss (loss function)

An instance on the wrong side of the line does not incur a penalty. ONLY when it's on the wrong side and outside of the margin.

12

Zero-one loss

An instance incurs a loss of 0 for a correct decision and 1 for an incorrect decision.

13

Squared error

Specifies a loss equal to the square of the distance from the boundary. A further instance would have a greater error. Usually used for numeric value prediction rather than classification.

14

Loss function

Determines how much penalty should be assigned to an instance based on the model's predictive value

15

Finish this sentence. Accuracy of training data is sometimes called...

In-sample accuracy (train) vs. out-of sample accuracy (test)

16

When is logistic regression more accurate vs. decision tree and vice versa?

LR is more accurate with a smaller data set, DT on bigger sets

17

What's the point of regularization?

It gives a penalty to more complicated models because those are more prone to overfitting.

18

In a confusion matrix what are the column headers? Row headers?

Column: Actual y and n
Row: Predicted y and n

19

False positive

Predicted positive, actual negative

20

False negative

Predicted negative, actual positive

21

True negative

Predicted negative, actual negative

22

True positive

Predicted positive, actual positive

23

True positive rate

True positive / all actual positives (both true and false)

24

False positive rate

False positive / all actual negatives (both true and false)

25

Positive predictive value (PPV)

True positive / all predicted positive (both correct and incorrect)

26

What's the expected value of a game of roulette? Probability of hitting black = 48%. Bet = $100

EV = (0.48)(100) + (1-0.48)(-100) = - 4

27

What are the two uses for expected value?

1. Inform how to use our classifier for individual predictions.
2. Compare classifiers.

28

Class priors

The proportion of positive and negative instances in your data set. Ex. 40 of 100 people would buy a new car next year if they could. p(p) = .4, p(n) = .6

29

Two critical conditions underlying profit calculations:

1. Class priors
2. Costs and benefits

30

Where is the perfect point on an ROC curve (hint: x axis is FPR, y axis is TPR)

Top left. FPR of 0, TPR of 1