Posts Tagged ‘ tree ’

Computational Time of Predictive Models

September 25, 2015
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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,…

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Choosing a Classifier

July 21, 2015
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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. >…

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Variable Selection using Cross-Validation (and Other Techniques)

July 1, 2015
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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…

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‘Variable Importance Plot’ and Variable Selection

June 17, 2015
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‘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).…

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Regression Models, It’s Not Only About Interpretation

March 22, 2015
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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…

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On Some Alternatives to Regression Models

March 20, 2015
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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…

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Les Arbres de Classification

April 18, 2014
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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 quantitative...

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ROC curves and classification

September 30, 2013
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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)…

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A Brief Tour of the Trees and Forests

April 29, 2013
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A Brief Tour of the Trees and Forests

Tree methods such as CART (classification and regression trees) can be used as alternatives to logistic regression. It is a way that can be used to show the probability of being in any hierarchical group. The following is a compilation of many of the key R packages that cover trees and forests.  The goal here […]

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Visualizing overdispersion (with trees)

February 13, 2013
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Visualizing overdispersion (with trees)

This week, we started to discuss overdispersion when modeling claims frequency. In my previous post, I discussed computations of empirical variances with different exposure. But I did use only one factor to compute classes. Of course, it is possible to use much more factors. For instance, using cartesian products of factors, > X=as.factor(paste(sinistres$carburant,sinistres$zone, + cut(sinistres$ageconducteur,breaks=c(17,24,40,65,101)))) > E=sinistres$exposition > Y=sinistres$nbre > vm=vv=ve=rep(NA,length(levels(X))) > for(i in 1:length(levels(X))){ + ve[i]=Ei=E[X==levels(X)[i]] + Yi=Y[X==levels(X)[i]] +…

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