Logistic Regression Flashcards

1
Q

What type of ML problems to linear classifiers solve?

A

Classification problems

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

What is the shape of the decision boundary for a linear classifier?

A

Line (2d), hyper plane (>2d)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

What function do we learn building a linear classifer?

A
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

For a linear classifer, how is the input x classified?

A

If F(x, w) > 0 then y = 1 else y = 0

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

What is the normal vector to a linear classifiers decision boundry?

A

w

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Describe the decision boundary of a linear classifier with set builder notation

A

{x | wTx + w0 = 0}

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

Definition of logisitics function

A

1 / (1 + exp(-z))

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

What is f(0) where f is the logistics function?

A

0.5

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

What is P(y=1 | x) for logistic regression?

A

f(wTx) where f is the logistic function

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

What is P(y=0 | x) for logistic regression?

A

1 - f(wTx) where f is the logistic function

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

What is the role of the logisitics function in logistics regression?

A

“squash” real line into [0,1] so we have a probablistic model

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

What is the significance of the magnitudes of w1, w2, …, wd for logistic regression?

A

How certain the classifications are:

small w’s - probabilitys close to 0.5

large w’s - probabilitys close to 0/1

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Whats the problem for maximizing L(w) for logistic regression?

A

No analytical solution, need to use numerical optimization

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

What is the score function for logisitc regression?

A

log likelihood

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

How is logistic regression extended more than 2 classes?

A
  • Create weight vector wk for each class k
  • Use softmax function
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Softmax function

A
17
Q

Whats the advantage of logisitic regression over linear regression (with 0/1 outputs)?

A

Less sensitive to outliers

18
Q

What type of ML problems does linear regression solve?

A

Classification

19
Q

Is logisitics regression descrimanitive or generative?

A

Discrimanitive

20
Q

What probability does logisitic regression model?

A

p( y | x )

y - output

x - input