I am working on a project by using a high dimensional data set. Close to 50000 Obs. with 392 Variable.
I used lasso to reduce it to this point from a total of 1200 variables. And the whole data set is grouped into several clusters. The data is of a product and its specifications and other relevant details as covariates and time to get sold is the response.
I am trying to predict the time with the help of covariates and I am using randomForestSRC package.
library(randomForestSRC) options(rf.cores = 8) rfdata <- data.frame(time,factors,agewitinmy,exclusivity,specs,financial) obj <- rfsrc(time ~ ., data = rfdata[(1:500), ],nsplit = 10) pre <- predict(obj,rfdata[(500:550), ]) results <- cbind(data.frame(time[500:550],pre$predicted))
But the accuracy is not good, am I doing something wrong here ? Can I adjust the accuracy by doing some changes. Or is it because of the high number of variables ?
Thanks for your time and response.