I have a dataset where the target variable is skewed left. When I plot a histogram of the log of this variable it's a nice, normal looking distribution. So I believe I should log transform it?
I tried that in my_model below. But when I evaluated it by looking at Mean Absolute Error I found that it under performed against the non log transformed version.
my_model <- lm(target ~ ,var1+var2+var3, data=ptrain)
my_model_log <- lm(log(target) ~ ,var1+var2+var3, data=ptrain)
my_predictions <- predict(my_model_log, interval="prediction", newdata=test_submission)
predict(), it will return an estimate for log (target). If you want an estimate for target you need to apply the inverse transformation,
exp(), to the predictions. The prediction interval may have interesting properties.