Session 26 TS, Predictive Analytics: Moving Out of Square One. Moderator: Jean-Marc Fix, FSA, MAAA

Transcription

1 Session 26 TS, Predictive Analytics: Moving Out of Square One Moderator: Jean-Marc Fix, FSA, MAAA Presenters: Jean-Marc Fix, FSA, MAAA Jeffery Robert Huddleston, ASA, CERA, MAAA

2 Predictive Modeling: Getting out of Life and Annuity Symposium Nashville, May 2016 Jean-Marc Fix, FSA, MAAA Vice President, R&D Optimum Re Insurance Jeff Huddleston, ASA, MAAA, CERA Senior Consultant Deloitte Consulting LLP

3 On to Square 2 Today s goal Things to have before you start Things to know before you start Starting The view from square 2 2

4 Today s Goal You have heard a lot about predictive modeling Time to get your feet wet Moving to square 2 3

5 Things to Have R (free!) Basic understanding of key concepts Data A question (for this lesson we reverse the logical order!) Patience Willingness to ask questions 4

6 Things to Know Basic R concepts Basic linear modeling concepts Basic statistical concepts 5

7 Starting Relevant ASOPs Document Use script Comments line start with # Keep script clean 6

8 What is R? For oldies: similarities to APL Don t think of it as a programming language to start Collection of functions extracted from useful packages Easy to dabble Lots of online resources ( Coursera) 7

9 Useful Packages and Functions Get R Finding functions: word of the net R-seek Quick-R R-blogger Loading package that has the functions you want install.package(packireallywant) library(packireallywant) 8

10 R Concepts Dataframe Categorical variables: factors Tidy data 9

11 Concept of Tidy Data Hadley Wickham tidyr package Tidy data row=observation, column=variable 10

12 Data Wrangling Get data Basic cleaning in excel First row: headers Variable names Avoid blank spaces in names First column: ID dplyr package (also by Wickham) 11

13 Clean Data Does data look as expected Remove quotation marks Consistent date format Clean trailing spaces Fill blank values or NA values with NA Save as CSV file Check CSV file in Notepad 12

14 Load Data Open R library(libraryname) #load libraries you will need Set working directory where your working file is getwd(), setwd() Use readcsv or readcsv2 functions Can also read directly from Excel 13

15 Useful Basic R Commands c(v1,v2,v3) concatenate x:y seq(min, max, by=5) sequence?fn()??fn() x<-5 14

16 Useful Basic R Commands ls() lists object in workspace rm(object) remove object rm(list=ls()) empties workspace cbind(v1,v2,v3) concatenate vectors in column rbind(v1,v2,v3) concatenate vectors in row? rep(x, times) 15

17 Useful Basic R Commands unique() runif( num, min, max) rnorm( num, mean, sd) as.date(as.character(textdate, format) as.factor(data$var1) as.data.frame(matrix that looks like a data frame) 16

18 Scripts ~ Programs Run from scripts File/new script Select what you want to run and press Ctr-r 17

19 Basic Useful Packages See script Ask around See r-bloggers community For development look into RStudio 18

20 Get Data a<-read.csv(filename.csv) 19

21 Explore Data class() names() head() tail() dataset$varname dataset[,varnumber] dataset[obsnumber,] 20

22 Explore Data str() summary() dim() length() For factors: unique(), levels() Conditions ==,!, 21

23 Data Craft Histogram Correlations See Regression diagnostics by John Fox 22

24 Why Explore? Anscombe s Quartet 23

25 Basic Graphic Exploration Histogram and charts hist(), qplot(), boxplot(), ggplot() Correlations summarize(), aggregate(), cor(), pairs.panels() 24

26 Split Data in Two Train set Test set Size vary from 50% train/50% set to 80/20 Want a decent size in test set Can be purely random, random by groups or by time 25

27 Easy splitting in R iris[iris$species %in% c( versicolor, virginica )] iris[iris[,5]== versicolor ]! is not and == is equal Split Use dplyr library Look at sample_n() and sample_frac() functions in dplyr 26

28 Why the split? Overfitting is the bane of the modeler! 27

29 Data 28

30 Model 1 29

31 Model 2 30

32 What s Next? 31

33 Components of GLM E(Y)=g -1 (βx) or g(e(y))=βx or g(y)=βx + ε Random error Link function Dependent variables generated by an exponential family distribution Independent variables 32

34 Choose Model Parameters Distribution for lapses: commonly used Poisson distribution Link function: default link for Poisson is log 33

35 Modeling Group Data Using an offset to adjust for exposure 34

36 Setting up the Model glm(y~offset(log(exposure)) +var1+var2, family=poisson(), data=yourdataframe) link function is implied as log when family is poisson() 35

37 Run First Iteration on Train Look at variables Pick the one with the most lift Run the model 36

38 Run First Iteration on Train Add a new variable Evaluate AIC - lower is better Is the complexity worth it? Repeat Variables can be interaction between variables or lagged or power of variables 37

39 You Think Your Model is Good? Look at the residuals Any patterns? 38

40 The Proof is in the (New) Pudding Now run your model on the test set? Does it still look good? 39

41 The View from Square 2 Pygmalion by Etienne Falconet 40

42 The End Look at the final model developed by Richard Xu and his team for the report Lapse Modeling for the Post-Level Period on the SOA website Pay special attention to the appendices Go forth and multiply model 41