Lasso is an obvious choice by it may also be interesting to look at the
variable importance from a random forest model
On 17 Dec 2015 17:28, "Manish MAHESHWARI" <mani...@dbs.com> wrote:
> Hi,
>
> I have a dataset with approx 400K Rows and 900 columns with a single
> dependent variable of 0/1 flag. The independent variables are both
> categorical and numerical. I have looked as SO/Cross Validated Posts but
> couldn't get an answer for this.
>
> Since I cannot try all possible combinations of variables or even attempt
> single model with all 900 columns, I am planning to create independent
> models of each variable using something like below -
>
> out = NULL
> xnames = colnames(train)[!colnames(train) %in% ignoredcols]
> for (f in xnames) {
> glmm = glm(train$conversion_flag ~ train[,f] - 1 , family = binomial)
> out =
> rbind.fill(out,as.data.frame(cbind(f,fmsb::NagelkerkeR2(glmm)[2]$R2)))
> out = rbind.fill(out,as.data.frame(cbind(f,'AIC',summary(glmm)$aic)))
> }
>
> This will give me the individual AIC and pseudo R2 for each of the
> variables. Post that I plan to select the variables with the best scores
> for both AIC and pseudoR2. Does this approach make sense?
>
> I obviously will use a nfold cross validation in the final model to ensure
> accuracy and avoid over fitting. However before I reach that I plan to use
> the above to select which variables to use.
>
> Thanks,
> Manish
> CONFIDENTIAL NOTE:
> The information contained in this email is intended on...{{dropped:13}}
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