#### Transcript Lecture 1: Introduction - City University of New York

```Lecture 4: Logistic Regression
Machine Learning
Today
• Bayesians v. Frequentists
• Logistic Regression
– Linear Model for Classification
1
Bayesians v. Frequentists
• What is a probability?
• Frequentists
– A probability is the likelihood that an event will happen
– It is approximated by the ratio of the number of observed events to the
number of total events
– Assessment is vital to selecting a model
– Point estimates are absolutely fine
• Bayesians
– A probability is a degree of believability of a proposition.
– Bayesians require that probabilities be prior beliefs conditioned on data.
– The Bayesian approach “is optimal”, given a good model, a good prior
and a good loss function. Don’t worry so much about assessment.
– If you are ever making a point estimate, you’ve made a mistake. The
only valid probabilities are posteriors based on evidence given some
prior
2
Logistic Regression
• Linear model applied to classification
• Supervised: target information is available
– Each data point xi has a corresponding target
ti.
• Goal: Identify a function
3
Target Variables
• In binary classification, it is convenient to represent ti
as a scalar with a range of [0,1]
– Interpretation of ti as the likelihood that xi is the member of
the positive class
– Used to represent the confidence of a prediction.
• For L > 2 classes, ti is often represented as a K
element vector.
– tij represents the degree of membership in class j.
– |ti| = 1
– E.g. 5-way classification vector:
4
Graphical Example of Classification
5
Decision Boundaries
6
Graphical Example of Classification
7
Classification approaches
• Generative
– Models the joint distribution
between c and x
– Highest data requirements
• Discriminative
– Fewer parameters to approximate
• Discriminant Function
– May still be trained probabilistically,
but not necessarily modeling a
likelihood.
8
Treating Classification as a
Linear model
9
Relationship between
Regression and Classification
• Since we’re classifying two classes, why not
set one class to ‘0’ and the other to ‘1’ then
use linear regression.
– Regression: -infinity to infinity, while class labels
are 0, 1
• Can use a threshold, e.g.
– y >= 0.5 then class 1
– y < 0.5 then class 2
1
Happy/Good/ClassA
f(x)>=0.5?
10
Odds-ratio
• Rather than thresholding, we’ll relate the
regression to the class-conditional
probability.
• Ratio of the odd of prediction y = 1 or y = 0
– If p(y=1|x) = 0.8 and p(y=0|x) = 0.2
– Odds ratio = 0.8/0.2 = 4
• Use a linear model to predict odds rather
than a class label.
11
Logit – Log odds ratio function
• LHS: 0 to infinity
• RHS: -infinity to
infinity
• Use a log function.
of dissolving the
easy manipulation
12
Logistic Regression
• A linear model used to predict log-odds
ratio of two classes
13
Logit to probability
14
Sigmoid function
• Squashing function to map the reals to a
finite domain.
15
Gaussian Class-conditional
• Assume the data is generated from a
gaussian distribution for each class.
• Leads to a bayesian formulation of logistic
regression.
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Bayesian Logistic Regression
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Maximum Likelihood Extimation
Logistic Regression
• Class-conditional Gaussian.
• Multinomial Class distribution.
• As ever, take the derivative of this
likelihood function w.r.t.
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Maximum Likelihood Estimation
of the prior
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Maximum Likelihood Estimation
of the prior
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Maximum Likelihood Estimation
of the prior
21
Discriminative Training
• Take the derivatives w.r.t.
– Be prepared for this for homework.
• In the generative formulation, we need to
estimate the joint of t and x.
– But we get an intuitive regularization
technique.
• Discriminative Training
– Model p(t|x) directly.
22
What’s the problem with
generative training
• Formulated this way, in D dimensions, this
function has D parameters.
• In the generative case, 2D means, and
D(D+1)/2 covariance values
• Quadratic growth in the number of
parameters.
• We’d rather linear growth.
23
Discriminative Training
24
Optimization
• Take the gradient in terms of w
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Optimization
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Optimization
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Optimization
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Optimization: putting it together
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Optimization
• We know the gradient of the error function,
but how do we find the maximum value?
• Setting to zero is nontrivial
• Numerical approximation
30
• Take a guess.
• Move in the direction of the negative
• Jump again.
• In a convex function this will converge
• Other methods include Newton-Raphson
31
Multi-class discriminant
functions
• Can extend to multiple classes
• Other approaches include constructing K-1
binary classifiers.
• Each classifier compares cn to not cn
• Computationally simpler, but not without
problems
32
Exponential Model
• Logistic Regression is a type of
exponential model.
– Linear combination of weights and features to
produce a probabilistic model.
33
Problems with Binary
Discriminant functions
34
K-class discriminant
35
Entropy
• Measure of uncertainty, or Measure of
“Information”
• High uncertainty equals high entropy.
• Rare events are more “informative” than
common events.
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Entropy
• How much information is received when
observing ‘x’?
• If independent, p(x,y) = p(x)p(y).
– H(x,y) = H(x) + H(y)
– The information contained in two unrelated
events is equal to their sum.
37
Entropy
• Binary coding of p(x): -log p(x)
– “How many bits does it take to represent a
value p(x)?”
– How many “decimal” places? How many
binary decimal places?
• Expected value of observed information
38
Examples of Entropy
• Uniform distributions have higher
distributions.
39
Maximum Entropy
• Logistic Regression is also known as
Maximum Entropy.
• Entropy is convex.
– Convergence Expectation.
• Constrain this optimization to enforce good
classification.
• Increase maximum likelihood of the data
while making the distribution of weights most
even.
– Include as many useful features as possible.
40
Maximum Entropy with
Constraints
•
From Klein and Manning Tutorial
41
Optimization formulation
• If we let the weights represent likelihoods
of value for each feature.
For each feature i
42
Solving MaxEnt formulation
• Convex optimization with a concave
objective function and linear constraints.
• Lagrange Multipliers
Dual representation of the
maximum likelihood estimation of
Logistic Regression
For each feature i
43
Summary
• Bayesian Regularization
– Introduction of a prior over parameters serves to
constrain weights
• Logistic Regression
–
–
–
–
Log odds to construct a linear model
Formulation with Gaussian Class Conditionals
Discriminative Training