Pour le second cours d’actuariat de l’assurance non-vie à l’ENSAE, qui aura lieu lundi après midi, les slides présentant les modèles classiques pour prédire des variables factorielles (classification) sont en ligne,

# Tag Archives: boosting

# “Improving Segmentation” (using Lorenz curves, or sort of)

This afternoon, André did send me an interesting graph about the use of Lorenz curve in the context of insurance pricing (and modeling)

It is some sort of Lorenz curve, upside-down, with on the x-axis the proportion of the population, and on the y-axis the proportion of the losses. The important point is that the population is sorted according the their risk, i.e. their premium. The code to generate such a curve is actually quite simple,

L <- function(u,varx="premium",vary="losses"){ base=base[order(base[,varx],decreasing=TRUE),] base$cum=(1:nrow(base))/nrow(base) return(sum(base[base$cum<=u,vary])/ sum(base[,vary]))} vu=seq(0,1,by=.01) vv=Vectorize(function(u) L(u))(vu)

My concern was more on two labels on the figure, with on the top-left “*perfect pricing*” and on the first diagonal “*average pricing*“. What could that possibly mean? Is there even such a thing as a “*perfect pricing*“? In order to understand what we plot here, let us generate some dataset, and fit some model. Including things that might be seen as the “*perfect model*“: the price base on the parameters used to generate the data, and the model used to generate the data, fitted on the data.

Continue reading “Improving Segmentation” (using Lorenz curves, or sort of)

# Choosing a Classifier

In order to illustrate the problem of chosing a classification model consider some simulated data,

> n = 500 > set.seed(1) > X = rnorm(n) > ma = 10-(X+1.5)^2*2 > mb = -10+(X-1.5)^2*2 > M = cbind(ma,mb) > set.seed(1) > Z = sample(1:2,size=n,replace=TRUE) > Y = ma*(Z==1)+mb*(Z==2)+rnorm(n)*5 > df = data.frame(Z=as.factor(Z),X,Y)

A first strategy is to split the dataset in two parts, a *training *dataset, and a *testing *dataset.

> df1 = training = df[1:300,] > df2 = testing = df[301:500,]

**The Holdout Method: Training and Testing Datasets**

The two datasets can be visualised below, with the training dataset on top, and the testing dataset below

> plot(df1$X,df1$Y,pch=19,col=c(rgb(1,0,0,.4), + rgb(0,0,1,.4))[df1$Z])

# An Update on Boosting with Splines

In my previous post, An Attempt to Understand Boosting Algorithm(s), I was puzzled by the boosting convergence when I was using some spline functions (more specifically linear by parts and continuous regression functions). I was using

> library(splines) > fit=lm(y~bs(x,degree=1,df=3),data=df)

The problem with that spline function is that knots seem to be fixed. The iterative boosting algorithm is

- start with some regression model
- compute the residuals, including some shrinkage parameter,

then the strategy is to model those residuals

- at step , consider regression
- update the residuals

and to loop. Then set

I thought that boosting would work well if at step , it was possible to change the knots. But the output

was quite disappointing: boosting does not improve the prediction here. And it looks like knots don’t change. Actually, if we select the ‘*best*‘ knots, the output is much better. The dataset is still

> n=300 > set.seed(1) > u=sort(runif(n)*2*pi) > y=sin(u)+rnorm(n)/4 > df=data.frame(x=u,y=y)

For an optimal choice of knot locations, we can use

> library(freeknotsplines) > xy.freekt=freelsgen(df$x, df$y, degree = 1, + numknot = 2, 555)

The code of the previous post can simply be updated

> v=.05 > library(splines) > xy.freekt=freelsgen(df$x, df$y, degree = 1, + numknot = 2, 555) > fit=lm(y~bs(x,degree=1,knots= + xy.freekt@optknot),data=df) > yp=predict(fit,newdata=df) > df$yr=df$y - v*yp > YP=v*yp > for(t in 1:200){ + xy.freekt=freelsgen(df$x, df$yr, degree = 1, + numknot = 2, 555) + fit=lm(yr~bs(x,degree=1,knots= + xy.freekt@optknot),data=df) + yp=predict(fit,newdata=df) + df$yr=df$yr - v*yp + YP=cbind(YP,v*yp) + } > nd=data.frame(x=seq(0,2*pi,by=.01)) > viz=function(M){ + if(M==1) y=YP[,1] + if(M>1) y=apply(YP[,1:M],1,sum) + plot(df$x,df$y,ylab="",xlab="") + lines(df$x,y,type="l",col="red",lwd=3) + fit=lm(y~bs(x,degree=1,df=3),data=df) + yp=predict(fit,newdata=nd) + lines(nd$x,yp,type="l",col="blue",lwd=3) + lines(nd$x,sin(nd$x),lty=2)} > viz(100)

I like that graph. I had the intuition that using (simple) splines would be possible, and indeed, we get a very smooth prediction.

# An Attempt to Understand Boosting Algorithm(s)

Last tuesday, at the annual meeting of the French Economic Association, I was having lunch with Alfred, and while we were chatting about modeling issues (econometric models against machine learning prediction), he asked me what boosting was. Since I could not be very specific, we’ve been looking at wikipedia webpage.

Boosting is a machine learning ensemble meta-algorithm for reducing bias primarily and also variance in supervised learning, and a family of machine learning algorithms which convert weak learners to strong ones

One should admit that it is not very informative. But at least, there is the idea that ‘weak learners’ can be used to provide a good predictor. Now, to be honest, I guess I understand the concept. But I still can’t reproduce what I got with standard ‘boosting’* *packages.

There are a lot of publications about the concept of ‘boosting’. In 1988, Michael Kearns published Thoughts on Hypothesis Boosting, which is probably the oldest one. About the algorithms, it is possible to find some references. Consider for instance Improving Regressors using Boosting Techniques, by Harris Drucker. Or The Boosting Approach to Machine Learning An Overview by Robert Schapire, among many others. In order to illustrate the use of boosting in the context of regression (and not classification, since I believe it provides a better visualisation) consider the section in Dong-Sheng Cao’s In The boosting: A new idea of building models.

Continue reading An Attempt to Understand Boosting Algorithm(s)

# Data Science: from Small to Big Data

This Tuesday, I will be in Leuven (in Belgium) at the ACP meeting to give a talk on **Data Science: from Small to Big Data**. The talk will take place in the Faculty Club from 6 till 8 pm. Slides could be found online (with animated pictures).

As usual, comments are welcome.

# On Some Alternatives to Regression Models

When you start discussing with people in machine learning, you quickly hear something like *“forget your econometric models, your GLMs, I can easily find a machine learning ‘model’ that can beat yours”. *I am usually very sceptical, especially when I hear “*easily*” or “*always*“. I have no problem about the fact that I use old econometric models, but I had the feeling that things aren’t that easy. I can understand that we might have problems when we do have a lot of features (I am still working on that, I’ll get back to this point soon), but **I have the feeling that I can still capture interactions, and non-linearities with standard econometric models as well as any machine learning algorithm**.

Just to illustrate, consider the following ‘*model*‘

where is (just to illustrate)

> n <- 5000 > rtf <- function(x1, x2) { sin(x1+x2)/(x1+x2) } > xgrid <- seq(1,6,length=31) > ygrid <- seq(1,6,length=31) > zgrid <- outer(xgrid,ygrid,rtf) > persp(xgrid,ygrid,zgrid,theta=30, phi=30, + col="green", ticktype="detailed",shade=TRUE)