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# Bayes Classifier, Linear Regression - PowerPoint PPT Presentation

Bayes Classifier , Linear Regression. 10701 /15781 Recitation Jan uary 29, 2008. Parts of the slides are from previous years’ recitation and lecture notes, and from Prof. Andrew Moore’s data mining tutorials. Classification and Regression. Classification

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### Bayes Classifier,Linear Regression

10701/15781 Recitation

January 29, 2008

Parts of the slides are from previous years’ recitation and lecture notes, and from Prof. Andrew Moore’s data mining tutorials.

• Classification

• Goal: Learn the underlying function

f: X (features)Y (class, or category)

e.g. words  “spam”, or “not spam”

• Regression

f: X (features)  Y (continuous values)

e.g. GPA  salary

Supervised Classification

• How to find an unknown function

f: X Y

(features  class)

or equivalently P(Y|X)

• Classifier:

• Find P(X|Y), P(Y), and use Bayes rule - generative

• Find P(Y|X) directly - discriminative

Learn P(Y|X)

1. Bayes rule:

P(Y|X) = P(X|Y)P(Y) / P(X) ~ P(X|Y)P(Y)

• Learn P(X|Y), P(Y)

• “Generative” classifier

2. Learn P(Y|X) directly

• “Discriminative”(to be covered later in class)

• e.g. logistic regression

• Generative Classifier: Bayes Classifier

Learn P(X|Y), P(Y)

• e.g. email classification problem

• 3 classes for Y = { spam, not spam, maybe }

• 10,000 binary features for X = {“Cash”, “Rolex”,…}

• How many parameters do we have?

• P(Y) :

• P(X|Y) :

Generative learning:Naïve Bayes

• Introduce conditional independence

P(X1,X2|Y) = P(X1 |Y) P(X2 |Y)

P(Y|X) = P(X|Y)P(Y) / P(X) for X=(Xi,…,Xn)

= P(X1|Y)…P(Xn|Y)P(Y) / P(X)

= prodi P(Xi|Y) P(Y) / P(X)

• Learn P(X1|Y), … P(Xn|Y), P(Y)

instead of learning P(X1,…, Xn |Y) directly

• 3 classes for Y = {spam, not spam, maybe}

• 10,000 binary features for X = {“Cash”,”Rolex”,…}

• Now, how many parameters?

• P(Y)

• P(X|Y)

• fewer parameters

• “simpler” – less likely to overfit

P(Y=1|(X1,X2)=(0,1))=?

• Full Bayes:

P(Y=1)=?

P((X1,X2)=(0,1)|Y=1)=?

• Naïve Bayes:

P(Y=1)=?

P((X1,X2)=(0,1)|Y=1)=?

• XOR

• Prediction of continuous variables

• e.g. I want to predict salaries from GPA.

•  I can regress that …

• Learn the mapping f: X  Y

• Model is linear in the parameters (+ some noise)

 linear regression

• Assume Gaussian noise

• Learn MLE Θ

• Normal linear regression

or equivalently,

• MLEΘ?

• MLE σ2 ?

• What if the inputs are vectors?

• Write matrix X and Y :

(n data points, k features for each data)

• MLE Θ =

• We may expect linear data that does not go through the origin

• Trick?

Regression: another example

• Assume the following model to fit the data. The model has one unknownparameter θ to be learned from data.

• A maximum likelihood estimation of θ?