Posts Tagged ‘ svm ’

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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Does Balancing Classes Improve Classifier Performance?

February 27, 2015
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Does Balancing Classes Improve Classifier Performance?

It’s a folk theorem I sometimes hear from colleagues and clients: that you must balance the class prevalence before training a classifier. Certainly, I believe that classification tends to be easier when the classes are nearly balanced, especially when the class you are actually interested in is the rarer one. But I have always been … Continue reading Does Balancing Classes Improve Classifier Performance? → Related posts: Don’t use correlation…

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Support Vector Machine with GPU, Part II

October 22, 2011
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Support Vector Machine with GPU, Part II

In our last tutorial on SVM training with GPU, we mentioned a necessary step to pre-scale the data with rpusvm-scale, and to reverse scaling the prediction outcome. This cumbersome procedure is now simplified with the latest RPUSVM. read more

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Support Vector Machine with GPU

August 28, 2011
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Support Vector Machine with GPU

Most elementary statistical inference algorithms assume that the data can be modeled by linear parameters with a normally distributed error component. A new class of algorithms called support vector machine (SVM) remove such constraint. read more

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