Tag Archives: tree

Classification on the German Credit Database

In our data science course, this morning, we’ve use random forrest to improve prediction on the German Credit Dataset. The dataset is

> url="http://freakonometrics.free.fr/german_credit.csv"
> credit=read.csv(url, header = TRUE, sep = ",")

Almost all variables are treated a numeric, but actually, most of them are factors,

> str(credit)
'data.frame':	1000 obs. of  21 variables:
 $ Creditability   : int  1 1 1 1 1 1 1 1 1 1 ...
 $ Account.Balance : int  1 1 2 1 1 1 1 1 4 2 ...
 $ Duration        : int  18 9 12 12 12 10 8  ...
 $ Purpose         : int  2 0 9 0 0 0 0 0 3 3 ...

(etc). Let us convert categorical variables as factors,

> F=c(1,2,4,5,7,8,9,10,11,12,13,15,16,17,18,19,20)
> for(i in F) credit[,i]=as.factor(credit[,i])

Let us now create our training/calibration and validation/testing datasets, with proportion 1/3-2/3

> i_test=sample(1:nrow(credit),size=333)
> i_calibration=(1:nrow(credit))[-i_test]

The first model we can fit is a logistic regression, on selected covariates

> LogisticModel <- glm(Creditability ~ Account.Balance + Payment.Status.of.Previous.Credit + Purpose + 
Length.of.current.employment + 
Sex...Marital.Status, family=binomial, 
data = credit[i_calibration,])

Based on that model, it is possible to draw the ROC curve, and to compute the AUC (on ne validation dataset)

> fitLog <- predict(LogisticModel,type="response",
+                   newdata=credit[i_test,])
> library(ROCR)
> pred = prediction( fitLog, credit$Creditability[i_test])
> perf <- performance(pred, "tpr", "fpr")
> plot(perf)
> AUCLog1=performance(pred, measure = "auc")@y.values[[1]]
> cat("AUC: ",AUCLog1,"\n")
AUC:  0.7340997

An alternative is to consider a logistic regression on all explanatory variables

> LogisticModel <- glm(Creditability ~ ., 
+  family=binomial, 
+  data = credit[i_calibration,])

We might overfit, here, and we should observe that on the ROC curve

> fitLog <- predict(LogisticModel,type="response",
+                   newdata=credit[i_test,])
> pred = prediction( fitLog, credit$Creditability[i_test])
> perf <- performance(pred, "tpr", "fpr")
> plot(perf)
> AUCLog2=performance(pred, measure = "auc")@y.values[[1]]
> cat("AUC: ",AUCLog2,"\n")
AUC:  0.7609792

There is a slight improvement here,  compared with the previous model, where only five explanatory variables were considered.

Consider now some regression tree (on all covariates)

> library(rpart)
> ArbreModel <- rpart(Creditability ~ ., 
+  data = credit[i_calibration,])

We can visualize the tree using

> library(rpart.plot)
> prp(ArbreModel,type=2,extra=1)

The ROC curve for that model is

> fitArbre <- predict(ArbreModel,
+                     newdata=credit[i_test,],
+                     type="prob")[,2]
> pred = prediction( fitArbre, credit$Creditability[i_test])
> perf <- performance(pred, "tpr", "fpr")
> plot(perf)
> AUCArbre=performance(pred, measure = "auc")@y.values[[1]]
> cat("AUC: ",AUCArbre,"\n")
AUC:  0.7100323

As expected, a single has a lower performance, compared with a logistic regression. And a natural idea is to grow several trees using some boostrap procedure, and then to agregate those predictions.

> library(randomForest)
> RF <- randomForest(Creditability ~ .,
+ data = credit[i_calibration,])
> fitForet <- predict(RF,
+                     newdata=credit[i_test,],
+                     type="prob")[,2]
> pred = prediction( fitForet, credit$Creditability[i_test])
> perf <- performance(pred, "tpr", "fpr")
> plot(perf)
> AUCRF=performance(pred, measure = "auc")@y.values[[1]]
> cat("AUC: ",AUCRF,"\n")
AUC:  0.7682367

Here this model is (slightly) better than the logistic regression. Actually, if we create many training/validation samples, and compare the AUC, we can observe that – on average – random forests perform better than logistic regressions,

> AUC=function(i){
+   set.seed(i)
+   i_test=sample(1:nrow(credit),size=333)
+   i_calibration=(1:nrow(credit))[-i_test]
+   LogisticModel <- glm(Creditability ~ ., 
+    family=binomial, 
+    data = credit[i_calibration,])
+   summary(LogisticModel)
+   fitLog <- predict(LogisticModel,type="response",
+                     newdata=credit[i_test,])
+   library(ROCR)
+   pred = prediction( fitLog, credit$Creditability[i_test])
+   AUCLog2=performance(pred, measure = "auc")@y.values[[1]] 
+   RF <- randomForest(Creditability ~ .,
+   data = credit[i_calibration,])
+   fitForet <- predict(RF,
+                       newdata=credit[i_test,],
+                       type="prob")[,2]
+   pred = prediction( fitForet, credit$Creditability[i_test])
+   AUCRF=performance(pred, measure = "auc")@y.values[[1]]
+   return(c(AUCLog2,AUCRF))
+ }
> A=Vectorize(AUC)(1:200)
> plot(t(A))

Econometrics vs. Machine Learning with Temporal Patterns

A few months ago, I did publish a (long) post entitled ‘some thoughts on economics, mathematics, econometrics, machine learning, etc‘. In that post, I was discussing possible differences between foundations of econometrics, and machine learning. I wanted to get back today on an important point, related to training/sampling datasets, when we have temporal data.

I was discussing this morning, with a student of the Data Science for Actuaries program, an interesting point related to claim frequency models, for insurance ratemaking. Since the goal is to predict claims frequency (to assess the level of the insurance premium), he suggested to use old data to train the model, and more recent one to test it. The problem is that the model did not incorporate any temporal pattern, and we got surprising results.

Consider here a simple dataset,

> set.seed(1)
> n=50000
> X1=runif(n)
> T=sample(2000:2015,size=n,replace=TRUE)
> L=exp(-3+X1-(T-2000)/20)
> E=rbeta(n,5,1)
> Y=rpois(n,L*E)
> B=data.frame(Y,X1,L,T,E)

Claims frequency is driven by a Poisson process, with one covariate, X1, and we assume that the intensity decreases (with an exponential rate). Consider here a standard linear regression, without any time effect

> reg=glm(Y~X1+offset(log(E)),data=B,
+ family=poisson)

We can also compute the empirical annualized claims frequency

> u=seq(0,1,by=.01)
> v=predict(reg,newdata=data.frame(X1=u,E=1))
> p=function(x){
+   B=B[abs(B$X1-x)<.1,]
+   sum(B$Y)/sum(B$E)
+ }
> vp=Vectorize(p)(seq(.05,.95,by=.1))

and plot the two curves on the same graph,

> plot(seq(.05,.95,by=.1),vp,type="b")
> lines(u,exp(v),lty=2,col="red")

This is what we usually do in econometrics. In machine learning, and more specifically to assess the quality of the model, and for model selection, it is common to split the dataset in two parts. A training sample, and a validation sample. Consider some randomized training/validation samples, then fit a model on the training sample, and finally use it to get a prediction,

> idx=sample(1:nrow(B),size=nrow(B)*7/8)
> B_a=B[idx,]
> B_t=B[-idx,]
> reg=glm(Y~X1+offset(log(E)),data=B_a,
+ family=poisson)
> u=seq(0,1,by=.01)
> v=predict(reg,newdata=data.frame(X1=u,E=1))
> p=function(x){
+   B=B_a[abs(B_a$X1-x)<.1,]
+   sum(B$Y)/sum(B$E)
+ }
> vp_a=Vectorize(p)(seq(.05,.95,by=.1))
> plot(seq(.05,.95,by=.1),vp_a,col="blue")
> lines(u,exp(v),lty=2)
> p=function(x){
+   B=B_t[abs(B_t$X1-x)<.1,]
+   sum(B$Y)/sum(B$E)
+ }
> vp_t=Vectorize(p)(seq(.05,.95,by=.1))
> lines(seq(.05,.95,by=.1),vp_t,col="red")

The blue curve is the prediction on the training sample (as we usually do in econometrics), but then the red curve is the prediction on the testing sample. Here, volatility probably comes from the small size of the testing sample (1 observation out of 8).

Now, what if we use the year as a splitting criteria : we fit a model on old years to fit a model, and we test it on recent years,

> B_a=subset(B,T<2014)
> B_t=subset(B,T>=2014)
> reg=glm(Y~X1+offset(log(E)),data=B_a,family=poisson)
> u=seq(0,1,by=.01)
> v=predict(reg,newdata=data.frame(X1=u,E=1))
> p=function(x){
+   B=B_a[abs(B_a$X1-x)<.1,]
+   sum(B$Y)/sum(B$E)
+ }
> vp_a=Vectorize(p)(seq(.05,.95,by=.1))
> plot(seq(.05,.95,by=.1),vp_a,col="blue")
> lines(u,exp(v),lty=2)
> p=function(x){
+   B=B_t[abs(B_t$X1-x)<.1,]
+   sum(B$Y)/sum(B$E)
+ }
> vp_t=Vectorize(p)(seq(.05,.95,by=.1))
> lines(seq(.05,.95,by=.1),vp_t,col="red")

Clearly, we miss something here…

We were looking at such a graph this morning, and it took me some time to understand how training and validation samples were designed, and that there was a possible temporal effect (actually, this morning, it was based on a 3 year training sample, and a 1 year validation sample).

Since there is a temporal pattern, let us capture it. As an econometrician, let me use a regression model

> reg=glm(Y~X1+T+offset(log(E)),data=B,
+ family=poisson)
> C=coefficients(reg)
> u=seq(1999,2016,by=.1)
> v=exp(-(u-2000)/20-3)
> plot(2000:2015,exp(C[1]+C[3]*(2000:2015)))
> lines(u,v,lty=2,col="red")

(I focus only on the evolution of the temporal variate on that graph).

Here, we use a linear model, but there are usually no reason to assume linearity. So we might consider splines

> library(splines)
> reg=glm(Y~X1+bs(T)+offset(log(E)),
+ data=B,family=poisson)
> u=seq(1999,2016,by=.1)
> v=exp(-(u-2000)/20-3)
> v2=predict(reg,newdata=data.frame(X1=0,
+ T=2000:2015,E=1))
> plot(2000:2015,exp(v2),type="b")
> lines(u,v,lty=2,col="red")

But here again, why should we assume that there is an underlying smooth function? There might be some ruptures… So let us consider a regression on factors

> reg=glm(Y~0+X1+as.factor(T)+offset(log(E)),
+ data=B,family=poisson)
> C=coefficients(reg)
> u=seq(1999,2016,by=.1)
> v=exp(-(u-2000)/20-3)
> plot(2000:2015,exp(C[2:17]),type="b")
> lines(u,v,lty=2,col="red")

An alternative might be to consider some more general model, like a regression tree

> library(rpart)
> reg=rpart(Y~X1+T+offset(log(E)),data=B,
+ method="poisson",cp=1e-4)
> p=function(t){
+   B=B[B$T==t,]
+   B$E=1
+   mean(predict(reg,newdata=B))
+ }
> y_m=Vectorize(function(t) p(t))(2000:2015)
> u=seq(1999,2016,by=.1)
> v=exp(-(u-2000)/20-3+.5)
> plot(2000:2015,y_m,ylim=c(.02,.085),type="b")
> lines(u,v,lty=2,col="red")

Here, it seems that something went wrong. I guess it’s coming from the exposure. So consider a simplier model, on the annualized frequency, and with weights that are related to the exposure

> reg=rpart(Y/E~X1+T,data=B,weights=B$E,cp=1e-4)
> p=function(t){
+   B=B[B$T==t,]
+   B$E=1
+   mean(predict(reg,newdata=B))
+ }
> y_m=Vectorize(function(t) p(t))(2000:2015)
> u=seq(1999,2016,by=.1)
> v=exp(-(u-2000)/20-3+.5)
> plot(2000:2015,y_m,ylim=c(.02,.085),type="b")
> lines(u,v,lty=2,col="red")

That was for the econometrician perspective. With a machine learning perspective, consider a training sample (here based on old data) and a validation sample (based on more recent ones)

> B_a=subset(B,T<2014)
> B_t=subset(B,T>=2014)

If we consider a model, it is easy to get a prediction on recent years, even if the model was designed to model older ones,

> reg_a=glm(Y~X1+T+offset(log(E)),
+ data=B_a,family=poisson)
> C=coefficients(reg_a)
> u=seq(1999,2016,by=.1)
> v=exp(-(u-2000)/20-3)
> plot(2000:2015,exp(C[1]+C[3]*c(2000:2013,
+ NA,NA)),type="b")
> lines(u,v,lty=2,col="red")
> points(2014:2015,exp(C[1]+C[3]*2014:2015),
+ pch=19,col="blue")

But if we use years as factors, things are more complicated.

> reg_a=glm(Y~0+X1+as.factor(T)+offset(log(E)),
+ data=B_a,family=poisson)
> C=coefficients(reg_a)
> RMSE=function(A){
+   L=exp(C[1]*B_t$X1+ A[1]*(B_t$T==2014) + A[2]*(B_t$T==2015))
+   Y_t=L*B_t$E
+   sum( (Y_t - B_t$Y )^2)}
> i=optim(c(.4,.4),RMSE)$par
> plot(2000:2015,c(exp(C[2:15]),NA,NA),)
> u=seq(1999,2016,by=.1)
> v=exp(-(u-2000)/20-3)
> lines(u,v,lty=2,col="red")
> points(2014:2015,exp(i),pch=19,col="blue")

becase we need to get a prediction on levels that were not in our training sample. Here, we minimize the RMSE to quantify factor levels for recent years. And the output is not that bad.

So yes, it is possible to get a training dataset on older data, and test it on recent years. But one should be careful, and take into account, properly, temporal patterns.

How Could Classification Trees Be So Fast on Categorical Variables?

I think that over the past months, I have been saying non-correct things about classification with categorical covariates. Because I never took time to look at it carefuly. Consider some simulated dataset, with a logistic regression,

> n=1e3
> set.seed(1)
> X1=runif(n)
> q=quantile(X1,(0:26)/26)
> q[1]=0
> X2=cut(X1,q,labels=LETTERS[1:26])
> p=exp(-.1+qnorm(2*(abs(.5-X1))))/(1+exp(-.1+qnorm(2*(abs(.5-X1)))))
> Y=rbinom(n,size=1,p)
> df=data.frame(X1=X1,X2=X2,p=p,Y=Y)

Here, we use some continuous covariate, except that is considered as not-observed. Instead, we have a categorical covariate with 26 categories. The (theoretical) relationship between the covariate and the probability is given below,

> vx1=seq(0,1,by=.001)
> vp=exp(-.1+qnorm(2*(abs(.5-vx1))))/(1+exp(-.1+qnorm(2*(abs(.5-vx1)))))
> plot(vx1,vp,type="l")

and the empirical probability, for each modality is

If we run a classification tree, we get

> library(rpart)
> tree=rpart(Y~X2,data=df)
> library(rpart.plot)
> prp(tree, type=2, extra=1)

To be more specific, the output is here

> tree
1) root 1000 249.90000 0.4900000  
  2) X2=F,G,H,I,J,K,L,M,N,O,P,Q,R 499 105.3 0.302
    4) X2=J,K,L,M,N,O,P,Q,R 346  65.12 0.25144  *
    5) X2=F,G,H,I 153  37.22876 0.4183007       *
  3) X2=A,B,C,D,E,S,T,U,V,W,X,Y,Z 501 109.61 0.67
    6) X2=B,C,D,E,S,T,U,V,W,X 385  90.38 0.623  *
    7) X2=A,Y,Z 116  14.50862 0.8534483         *

 

Note that it takes less than a second to get that output. So clearly, we did not look for all combinations between modalities. For the first node, there are like  possible groups, i.e.

> 67108864

It is big… not huge, but too big to try all combinations, since that’s only the first node, and we have to do it again on the two leaves, etc. Antoine (aka @ly_antoine) told me – while we were having a coffee after lunch today – the trick to get a fast algorithm, on categories. And as usual, the idea is very clever…

First, we need a function to compute Gini index

> gini=function(y,classe){
+    T=table(y,classe)
+    nx=apply(T,2,sum)
+    n=sum(T)
+    pxy=T/matrix(rep(nx,each=2),nrow=2)
+    omega=matrix(rep(nx,each=2),nrow=2)/n
+    g=-sum(omega*pxy*(1-pxy))
+    return(g)}

For the first node, the idea is very simple:

  • Compute empirical averages 
> cond_prob=aggregate(df$Y,by=list(df$X2),mean)
  • Then sort those values, ,
  • Based on that ordering, consider 
> Group_Letters=cond_prob[order(cond_prob$x),2]

  • Then consider (only)  possible partitions,

against 

> v_gini=rep(NA,26)
> for(v in 1:26){
+   CLASSE=df$X2 %in% Group_Letters[1:v]
+   v_gini[v]=gini(y=df$Y,classe=CLASSE)
+ }

If we plot them, we get

> plot(1:26,v_gini,type="b)

As for continuous variables, we seek for the maximum value, and then, we have our two groups,

> sort(Group_Letters[1:which.max(v_gini)])
 [1] F G H I J K L M N O P Q R

That’s exactly what we got with the tree function in R,

1) root 1000 249.90000 0.4900000  
  2) X2=F,G,H,I,J,K,L,M,N,O,P,Q,R 499 105.30 0.30

Now, consider the leaf on the left (for instance)

> sub_df=df[df$X2 %in% sort(Group_Letters[1:which.max(v_gini)]),]

Then use the same algorithm as before: sort the conditional means,

> cond_prob=aggregate(sub_df$Y,by=
+ list(sub_df$X2),mean)
> s_Group_Letters=cond_prob[order(cond_prob$x),2]

Then compute Gini indices based on groups obtained from that ordering,

> v_gini=rep(NA,length(sub_Group_Letters))
> for(v in 1:length(sub_Group_Letters)){
+   CLASSE=sub_df$X2 %in% s_Group_Letters[1:v]
+   v_gini[v]=gini(y=sub_df$Y,classe=CLASSE)
+ }

If we plot it, we get our two groups,

> plot(1:length(s_Group_Letters),v_gini,type="b")

And the first group is here

> sort(sub_Group_Letters[1:which.max(v_gini)])
[1] J K L M N O P Q R

Again, that’s exactly what we got with the R function

1) root 1000 249.90000 0.4900000  
  2) X2=F,G,H,I,J,K,L,M,N,O,P,Q,R 499 105.30 0.30
    4) X2=J,K,L,M,N,O,P,Q,R 346  65.12 0.25144  *

Clever, isn’t?

Computational Time of Predictive Models

Tuesday, at the end of my 5-hour crash course on machine learning for actuaries, Pierre asked me an interesting question about computational time of different techniques. I’ve been presenting the philosophy of various algorithm, but I forgot to mention computational time. I wanted to try several classification algorithms on the dataset used to illustrate the techniques

> rm(list=ls())
> myocarde=read.table(
"http://freakonometrics.free.fr/myocarde.csv",
head=TRUE,sep=";")
> levels(myocarde$PRONO)=c("Death","Survival")

But the dataset is rather small, with 71 observations and 7 explanatory variables. So I decided to replicate the observations, and to add some covariates,

> levels(myocarde$PRONO)=c("Death","Survival")
> idx=rep(1:nrow(myocarde),each=100)
> TPS=matrix(NA,30,10)
> myocarde_large=myocarde[idx,]
> k=23
> M=data.frame(matrix(rnorm(k*
+ nrow(myocarde_large)),nrow(myocarde_large),k))
> names(M)=paste("X",1:k,sep="")
> myocarde_large=cbind(myocarde_large,M)
> dim(myocarde_large)
[1] 7100   31
> object.size(myocarde_large)
2049.064 kbytes

The dataset is not big… but at least, it does not take 0.0001 sec. to run a regression.  Actually, to run a logistic regression, it takes 0.1 second

> system.time(fit< glm(PRONO~.,
+ data=myocarde_large, family="binomial"))
       user      system     elapsed 
      0.114       0.016       0.134 
> object.size(fit)
9,313.600 kbytes

And I was surprised that the regression object was 9Mo, which is more than four times the size of the dataset. With a large dataset, 100 times larger,

> dim(myocarde_large_2)
[1] 710000     31

it takes 20 sec.

> system.time(fit<-glm(PRONO~.,
+ data=myocarde_large_2, family="binomial"))
utilisateur     système      écoulé 
     16.394       2.576      19.819 
> object.size(fit)
90,9025.600 kbytes

and the object is ‘only’ ten times bigger.

Continue reading Computational Time of Predictive Models

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])

Continue reading Choosing a Classifier

Variable Selection using Cross-Validation (and Other Techniques)

A natural technique to select variables in the context of generalized linear models is to use a stepŵise procedure. It is natural, but contreversial, as discussed by Frank Harrell  in a great post, clearly worth reading. Frank mentioned about 10 points against a stepwise procedure.

  • It yields R-squared values that are badly biased to be high.
  • The F and chi-squared tests quoted next to each variable on the printout do not have the claimed distribution.
  • The method yields confidence intervals for effects and predicted values that are falsely narrow (see Altman and Andersen (1989)).
  • It yields p-values that do not have the proper meaning, and the proper correction for them is a difficult problem.
  • It gives biased regression coefficients that need shrinkage (the coefficients for remaining variables are too large (see Tibshirani (1996)).
  • It has severe problems in the presence of collinearity.
  • It is based on methods (e.g., F tests for nested models) that were intended to be used to test prespecified hypotheses.
  • Increasing the sample size does not help very much (see Derksen and Keselman (1992)).
  • It allows us to not think about the problem.
  • It uses a lot of paper.

Continue reading Variable Selection using Cross-Validation (and Other Techniques)

‘Variable Importance Plot’ and Variable Selection

Classification trees are nice. They provide an interesting alternative to a logistic regression.  I started to include them in my courses maybe 7 or 8 years ago. The question is nice (how to get an optimal partition), the algorithmic procedure is nice (the trick of splitting according to one variable, and only one, at each node, and then to move forward, never backward), and the visual output is just perfect (with that tree structure). But the prediction can be rather poor. The performance of that algorithme can hardly compete with a (well specified) logistic regression.

Then I discovered forests (see Leo Breiman’s page for a detailed presentation). Being a huge fan of boostrap procedures I loved the idea. In regression models, I usually mention boostrap to avoid asymptotic approximations: we boostrap the rows (the observations). In the case of random forest, I have to admit that the idea of selecting randomly a set of possible variables at each node is very clever. The performance is much better, but interpretation is usually more difficult. And something that I love when there are a lot of covariance, the variable importance plot. Which is something that we can hardly get with econometric models (please let me know if I’m wrong).

In order to illustrate, let us generate a large dataset. Not necessarily huge, but large, so that we really have to select variables.  Since it is more interesting if we have possibly correlated variables, we need a covariance matrix. There is a nice package in R to randomly generate covariance matrices.

> set.seed(1)
> n=500
> library(clusterGeneration)
> library(mnormt)
> S=genPositiveDefMat("eigen",dim=15)
> S=genPositiveDefMat("unifcorrmat",dim=15)
> X=rmnorm(n,varcov=S$Sigma)
> library(corrplot)
> corrplot(cor(X), order = "hclust")

See Gosh & Hendersen (2003) for more details on the methodology.

Continue reading ‘Variable Importance Plot’ and Variable Selection

Spliting a Node in a Tree

If we grow a tree with standard functions in R, on the same dataset used to introduce classification tree in some previous post,

> MYOCARDE=read.table(
+ "http://freakonometrics.free.fr/saporta.csv",
+ head=TRUE,sep=";")
> library(rpart)
> cart<-rpart(PRONO~.,data=MYOCARDE)

we get

> library(rpart.plot)
> library(rattle)
> prp(cart,type=2,extra=1)

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Regression Models, It’s Not Only About Interpretation

Yesterday, I did upload a post where I tried to show that “standard” regression models where not performing bad. At least if you include splines (multivariate splines) to take into accound joint effects, and nonlinearities. So far, I do not discuss the possible high number of features (but with boostrap procedures, it is possible to assess something related to variable importance, that people from machine learning like).

But my post was not complete: I was simply plotting the prediction obtained by some model. And it “looked like” the regression was nice, but so were the random forrest, the https://latex.codecogs.com/gif.latex?k-nearest neighbour and boosting algorithm. What if we compare those models on new data?

Continue reading Regression Models, It’s Not Only About Interpretation

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

https://latex.codecogs.com/gif.latex?\mathbb{E}[Y\vert\boldsymbol{X}=\boldsymbol{x}]=m(\boldsymbol{x})

where https://latex.codecogs.com/gif.latex?m(\cdot) 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)

Continue reading On Some Alternatives to Regression Models

Growing one Tree

Consider the following toy dataset, with some spam/ham information, and two words, “viagra” and “lottery”.

> load(spam.RData)
> head(db)
      Y viagra lottery
27 spam      0       1
37  ham      0       1
57 spam      0       0
89  ham      0       0
20 spam      1       0
86  ham      0       0

For the first node, compute Gini index for the two variables,

> gini=function(variable){
+ T=table(db$Y,db[,variable])
+ nx=apply(T,2,sum)
+ ProbCond=T/matrix(rep(nx,each=2),2,2)
+ ProbCond
+ Gini=-ProbCond*(1-ProbCond)
+ sum(matrix(rep(nx,each=2),2,2)/sum(nx)*Gini)}
> gini("viagra")
[1] -0.44
> gini("lottery")
[1] -0.487

Here Gini index is maximal for “viagra”, so that will be the first node.

Continue reading Growing one Tree

Les Arbres de Classification

J’animerai une formation lundi 28 de 14:00 à 16:00 au local N-6320 de l’UQAM sur le thème introduction aux arbres de classification. Cette formation est organisée dans le cadre des séminaires en méthodes d’analyses quantitatives et qualitatives qui se tiennent régulièrement depuis un peu plus d’un mois. animé par le collectif pour le développement et les applications en mesure et évaluation (Cdame). Les slides sont disponibles en pdf (il y a quelques animations, qui ne passent qu’avec Acrobat)

La base utilisée tout au long des exposés est la suivante
> MYOCARDE=read.table("http://freakonometrics.free.fr/saporta.csv",head=TRUE,sep=";")

ROC curves and classification

To get back to a question asked after the last course (still on non-life insurance), I will spend some time to discuss ROC curve construction, and interpretation. Consider the dataset we’ve been using last week,

> db = read.table("http://freakonometrics.free.fr/db.txt",header=TRUE,sep=";")
> attach(db)

The first step is to get a model. For instance, a logistic regression, where some factors were merged together,

> X3bis=rep(NA,length(X3))
> X3bis[X3%in%c("A","C","D")]="ACD"
> X3bis[X3%in%c("B","E")]="BE"
> db$X3bis=as.factor(X3bis)
> reg=glm(Y~X1+X2+X3bis,family=binomial,data=db)

From this model, we can predict a probability, not a  variable,

> S=predict(reg,type="response")

Let https://latex.codecogs.com/gif.latex?\widehat{S} denote this variable (actually, we can use the score, or the predicted probability, it will not change the construction of our ROC curve). What if we really want to predict a  variable. As we usually do in decision theory. The idea is to consider a threshold https://perso.univ-rennes1.fr/arthur.charpentier/latex/ROC-04.png, so that

  • if https://perso.univ-rennes1.fr/arthur.charpentier/latex/ROC-05.png, then  https://perso.univ-rennes1.fr/arthur.charpentier/latex/ROC-02.png will be https://latex.codecogs.com/gif.latex?1, or “positive” (using a standard terminology)
  • si https://perso.univ-rennes1.fr/arthur.charpentier/latex/ROC-06.png, then  https://perso.univ-rennes1.fr/arthur.charpentier/latex/ROC-02.png will be https://latex.codecogs.com/gif.latex?0, or “negative

Then we derive a contingency table, or a confusion matrix

     observed value https://perso.univ-rennes1.fr/arthur.charpentier/latex/ROC-01.png
predicted
value
https://perso.univ-rennes1.fr/arthur.charpentier/latex/ROC-02.png
“positive“ “négative“
“positive“ TP FP
“négative“ FN TN

where TP are the so-called true positive, TN  the true negative, FP are the false positive (or type I error) and FN are the false negative (type II errors). We can get that contingency table for a given threshold https://perso.univ-rennes1.fr/arthur.charpentier/latex/ROC-04.png

> roc.curve=function(s,print=FALSE){
+ Ps=(S>s)*1
+ FP=sum((Ps==1)*(Y==0))/sum(Y==0)
+ TP=sum((Ps==1)*(Y==1))/sum(Y==1)
+ if(print==TRUE){
+ print(table(Observed=Y,Predicted=Ps))
+ }
+ vect=c(FP,TP)
+ names(vect)=c("FPR","TPR")
+ return(vect)
+ }
> threshold = 0.5
> roc.curve(threshold,print=TRUE)
        Predicted
Observed   0   1
       0   5 231
       1  19 745
      FPR       TPR 
0.9788136 0.9751309

Here, we also compute the false positive rates, and the true positive rates,

  • TPR = TP / P = TP / (TP + FN) also called sentivity, defined as the rate of true positive: probability to be predicted positve, given that someone is positive (true positive rate)
  • FPR = FP / N = FP / (FP + TN) is the rate of false positive: probability to be predicted positve, given that someone is negative (false positive rate)

The ROC curve is then obtained using severall values for the threshold. For convenience, define

> ROC.curve=Vectorize(roc.curve)

First, we can plot https://latex.codecogs.com/gif.latex?(\widehat{S}_i,Y_i) (a standard predicted versus observed graph), and visualize true and false positive and negative, using simple colors

> I=(((S>threshold)&(Y==0))|((S<=threshold)&(Y==1)))
> plot(S,Y,col=c("red","blue")[I+1],pch=19,cex=.7,,xlab="",ylab="")
> abline(v=threshold,col="gray")

And for the ROC curve, simply use

> M.ROC=ROC.curve(seq(0,1,by=.01))
> plot(M.ROC[1,],M.ROC[2,],col="grey",lwd=2,type="l")

This is the ROC curve. Now, to see why it can be interesting, we need a second model. Consider for instance a classification tree

> library(tree)
> ctr <- tree(Y~X1+X2+X3bis,data=db)
> plot(ctr)
> text(ctr)

To plot the ROC curve, we just need to use the prediction obtained using this second model,

> S=predict(ctr)

All the code described above can be used. Again, we can plot https://latex.codecogs.com/gif.latex?(\widehat{S}_i,Y_i) (observe that we have 5 possible values for https://latex.codecogs.com/gif.latex?\widehat{S}_i, which makes sense since we do have 5 leaves on our tree). Then, we can plot the ROC curve,

An interesting idea can be to plot the two ROC curves on the same graph, in order to compare the two models

> plot(M.ROC[1,],M.ROC[2,],type="l")
> lines(M.ROC.tree[1,],M.ROC.tree[2,],type="l",col="grey",lwd=2)

The most difficult part is to get a proper interpretation. The tree is not predicting well in the lower part of the curve. This concerns people with a very high predicted probability. If our interest is more on those with a probability lower than 90%, then, we have to admit that the tree is doing a good job, since the ROC curve is always higher, comparer with the logistic regression.