Download presentation

Presentation is loading. Please wait.

Published byCesar Mayne Modified over 2 years ago

1
Week 3

2
Logistic Regression Overview and applications Additional issues Select Inputs Optimize complexity Transforming Inputs

3
Estimating relation between different variables dependent variables and independent variables change in DV for any change in IV Applications forecasting, healthcare, economics, finance

4
Whether someone will respond or not to advertisements? Whether someone is a high default risk on a loan? Whether someone will buy or not buy? Whether the patient will responds to treatment or not? Whether a machine will fail next week?

5
Regression Analysis where DV is binary (0/1) – most common case Classify a new observation into a class based on its predictors Predictors can be categorical or continuous

6
Probability Odds Logit function Logistic function

9
Specification

10
Specify the logistic function Estimate the parameter βs Substitute the value of βs in model to estimate odds ratio = β 0 + β 1 x 1 + β 2 x 2 ·· ^ log p 1 – p () ^

11
Odds ratio : Amount odds change with unit change in input. 1 odds exp(β i ) Δx i consequence... = β 0 + β 1 x 1 + β 2 x 2 ·· ^ log p 1 – p () ^

12
Can the categories be correctly predicted given a set of predictors? What is the relative importance of each predictor? Which predictors have a ‘statistically significant effect’?

14
Entry Cutoff Input p -value...

15
Entry Cutoff Input p -value...

16
Entry Cutoff Input p -value...

17
Entry Cutoff Input p -value...

18
Entry Cutoff Input p -value

19
Stay Cutoff Input p -value...

20
Stay Cutoff Input p -value...

21
Stay Cutoff Input p -value...

22
Stay Cutoff Input p -value...

23
Stay Cutoff Input p -value...

24
Stay Cutoff Input p -value...

25
Stay Cutoff Input p -value...

26
Stay Cutoff Input p -value

27
Entry Cutoff Stay Cutoff...

28
Input p -value Entry Cutoff Stay Cutoff...

29
Input p -value Entry Cutoff Stay Cutoff...

30
Input p -value Entry Cutoff Stay Cutoff...

31
Input p -value Entry Cutoff Stay Cutoff...

32
Input p -value Entry Cutoff Stay Cutoff...

33
Input p -value Entry Cutoff Stay Cutoff

35
1 2 3 4 5 6 Model fit statistic training validation...

36
123456 Model fit statistic Evaluate each sequence step....

38
high leverage points skewed input distribution standard regression true association standard regression true association Original Input Scale...

39
high leverage points skewed input distribution standard regression true association standard regression true association Original Input Scale more symmetric distribution Regularized Scale...

40
Original Input Scale more symmetric distribution Regularized Scale standard regression... Original Input Scale high leverage points skewed input distribution

41
Regularized Scale standard regression... Original Input Scale regularized estimate

42
Regularized Scale standard regression... Original Input Scale regularized estimate true association

Similar presentations

OK

LOGISTIC REGRESSION Binary dependent variable (pass-fail) Odds ratio: p/(1-p) eg. 1/9 means 1 time in 10 pass, 9 times fail Log-odds ratio: y = ln[p/(1-p)]

LOGISTIC REGRESSION Binary dependent variable (pass-fail) Odds ratio: p/(1-p) eg. 1/9 means 1 time in 10 pass, 9 times fail Log-odds ratio: y = ln[p/(1-p)]

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on sectors of economy for class 10 Ppt on bluetooth hacking statistics Ppt on breast cancer awareness Ppt on credit default swaps index Ppt on carburetor parts Ppt on mobile computing pdf Ppt on alternative sources of energy to drive vehicles Ppt on world book day ideas Ppt on solar system for class 5 Ppt on product advertising ideas