Machine Learning Logistic Regression Jeff Howbert Introduction to

Machine Learning Logistic Regression Jeff Howbert Introduction to Machine Learning Winter 2014 1

Logistic regression Name is somewhat misleading. Really a technique for classification, not regression. – “Regression” comes from fact that we fit a linear model to the feature space. l Involves a more probabilistic view of classification. l Jeff Howbert Introduction to Machine Learning Winter 2014 2

Different ways of expressing probability Consider a two-outcome probability space, where: – p( O 1 ) = p – p( O 2 ) = 1 – p = q l Can express probability of O 1 as: l standard probability odds log odds (logit) Jeff Howbert notation p p/q log( p / q ) range equivalents 0 0. 5 1 0 1 + - 0 + Introduction to Machine Learning Winter 2014 3

Log odds Numeric treatment of outcomes O 1 and O 2 is equivalent – If neither outcome is favored over the other, then log odds = 0. – If one outcome is favored with log odds = x, then other outcome is disfavored with log odds = -x. l Especially useful in domains where relative probabilities can be miniscule – Example: multiple sequence alignment in computational biology l Jeff Howbert Introduction to Machine Learning Winter 2014 4

From probability to log odds (and back again) Jeff Howbert Introduction to Machine Learning Winter 2014 5

Standard logistic function Jeff Howbert Introduction to Machine Learning Winter 2014 6

Logistic regression l Scenario: – A multidimensional feature space (features can be categorical or continuous). – Outcome is discrete, not continuous. u We’ll focus on case of two classes. – It seems plausible that a linear decision boundary (hyperplane) will give good predictive accuracy. Jeff Howbert Introduction to Machine Learning Winter 2014 7

Using a logistic regression model l Model consists of a vector in d-dimensional feature space l For a point x in feature space, project it onto to convert it into a real number z in the range - to + l Map z to the range 0 to 1 using the logistic function l Overall, logistic regression maps a point x in ddimensional feature space to a value in the range 0 to 1 Jeff Howbert Introduction to Machine Learning Winter 2014 8

Using a logistic regression model l Can interpret prediction from a logistic regression model as: – A probability of class membership – A class assignment, by applying threshold to probability threshold represents decision boundary in feature space u Jeff Howbert Introduction to Machine Learning Winter 2014 9

Training a logistic regression model l Need to optimize so the model gives the best possible reproduction of training set labels – Usually done by numerical approximation of maximum likelihood – On really large datasets, may use stochastic gradient descent Jeff Howbert Introduction to Machine Learning Winter 2014 10

Logistic regression in one dimension Jeff Howbert Introduction to Machine Learning Winter 2014 11

Logistic regression in one dimension Jeff Howbert Introduction to Machine Learning Winter 2014 12

Logistic regression in one dimension l Parameters control shape and location of sigmoid curve – controls location of midpoint – controls slope of rise Jeff Howbert Introduction to Machine Learning Winter 2014 13

Logistic regression in one dimension Jeff Howbert Introduction to Machine Learning Winter 2014 14

Logistic regression in one dimension Jeff Howbert Introduction to Machine Learning Winter 2014 15

Logistic regression in two dimensions Subset of Fisher iris dataset – Two classes – First two columns (SL, SW) Jeff Howbert Introduction to Machine Learning decision boundary Winter 2014 16

Logistic regression in two dimensions Interpreting the model vector of coefficients l From MATLAB: B = [ 13. 0460 l = B( 1 ), = [ 1 2 ] = B( 2 : 3 ) l , define location and orientation of decision boundary l -1. 9024 – - is distance of decision boundary from origin – decision boundary is perpendicular to magnitude of defines gradient of probabilities between 0 and 1 Jeff Howbert Introduction to Machine Learning -0. 4047 ] Winter 2014 17

Logistic regression in two dimensions Jeff Howbert Introduction to Machine Learning Winter 2014 18

Heart disease dataset l l l 13 attributes (see heart. docx for details) – 2 demographic (age, gender) – 11 clinical measures of cardiovascular status and performance 2 classes: absence ( 1 ) or presence ( 2 ) of heart disease 270 samples Dataset taken from UC Irvine Machine Learning Repository: http: //archive. ics. uci. edu/ml/datasets/Statlog+(Heart) Preformatted for MATLAB as heart. mat. Jeff Howbert Introduction to Machine Learning Winter 2014 19

MATLAB interlude matlab_demo_05. m Jeff Howbert Introduction to Machine Learning Winter 2014 20

Logistic regression l Advantages: – Makes no assumptions about distributions of classes in feature space – Easily extended to multiple classes (multinomial regression) – Natural probabilistic view of class predictions – Quick to train – Very fast at classifying unknown records – Good accuracy for many simple data sets – Resistant to overfitting – Can interpret model coefficients as indicators of feature importance l Disadvantages: – Linear decision boundary Jeff Howbert Introduction to Machine Learning Winter 2014 21