# Classification using Logistic Regression

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1 Classification using Logistic Regression Ingmar Schuster Patrick Jähnichen using slides by Andrew Ng Institut für Informatik

2 This lecture covers Logistic regression hypothesis Decision Boundary Cost function (why we need a new one) Simplified Cost function & Gradient Descent Advanced Optimization Algorithms Multiclass classification Logistic regression 2

3 Logistic regression Hypothesis Representation Logistic regression 3

4 Classification Problems Classification malignant or benign cancer Spam or Ham Human face or no human face Positive Sentiment? Binary Decision Task (in most simple case) Want Data point belongs to class if close to 1 Doesn't belong to class if close to 0 Logistic regression 4

5 Logistic Function (Sigmoid Function) maps into interval [0;1] 0 asymptote for 1 asymptote for Sigmoid Function (S-shape) Logistic Function Logistic regression 5

6 Hypothesis Interpretation Because probabilites should sum to 1, define If interpret as 70% chance data point belongs to class If classify as positive sentiment, malignant tumor,... Logistic regression 6

7 Logistic regression Decision boundary Logistic regression 7

8 If or equivalently predict y = 1 If or equivalently predict y = 0 Logistic regression 8

9 Example If and Prediction y = 1 whenever Logistic regression 9

10 Example If and Prediction y = 1 whenever Logistic regression 10

11 Logistic regression Cost Function Logistic regression 11

12 Training and cost function Training data wih m datapoints, n features where Average cost Logistic regression 12

13 Reusing Linear Regression cost Cost from linear regression with logistic regression hypothesis leads to non-convex average cost Convex J easier to optimize (no local optima) All function values below intersection with any line 13

14 Logistic Regression Cost function If y = 1 and h(x) = 1, Cost = 0 But for Corresponds to intuition: if prediction is h(x) = 0 but actual value was y = 1, learning algorithm will be penalized by large cost Logistic regression 14

15 Logistic Regression Cost function If y = 0 and h(x) = 0, Cost = 0 But for Logistic regression 15

16 Logistic regression Simplified Cost Function & Gradient Descent Logistic regression 16

17 Simplified Cost Function (1) Original cost of single training example Because we always have y = 0 or y = 1 we can simplify the cost function definition to To convince yourself, use the simplified cost function to calculate Logistic regression 17

18 Simplified Cost Function (2) Cost function for training set Find parameter argument that minimizes J: To make predictions given new x output Logistic regression 18

19 Gradient Descent for logistic regression Gradient Descent to minimize logistic regression cost function with identical algorithm as for linear regression Logistic regression 19

21 Advanced Optimization Algorithms Given functions to compute an optimization algorithm will compute Optimization Algorithms (Gradient Descent) Conjugate Gradient BFGS & L-BFGS Advantages Often faster convergence No learning rate to choose Disadvantages Complex Logistic regression 21

22 Preimplemented Alorithms Advanced optimization algorithms exist already in Machine Learning packages for important languages Octave/Matlab R Java Rapidminer under the hood Logistic regression 22

23 Multiclass Classification (by cheap trickery) Logistic regression 23

24 Multiclass classification problems Classes of s: Work, Friends, Invoices, Job Offers Medical diagnosis: Not ill, Asthma, Lung Cancer Weather: Sunny, Cloudy, Rain, Snow Number classes as 1, 2, 3,... Logistic regression 24

25 Binary vs. Multiclass Classification Logistic regression 25

26 One versus all Logistic regression 26

27 Train logistic regression classifier for each class i to predict probability of y = i On new x predict class i which satisfies Logistic regression 27

28 This lecture covered Logistic regression hypothesis Decision Boundary Cost function(why we need a new one) Simplified Cost function & Gradient Descent Advanced Optimization Algorithms Multiclass classification Logistic regression 28

29 Pictures Tumor picture by flickr-user bc the path, License CC SA NC Lightbulb picture from openclipart.org, public domain Machine Learning Introduction 29

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