I'm beginning my data science journey and I've faced a challenge that confuses me a bit. I have a set with few features and a target variable whose raw distribution is highly skewed.
I've read that it's possible to use a log transformation to normalize the target variable (loss in $) and thus increase the accuracy.
When I train my model with "y_raw", using MAE I get an error of 306k. When I log-transform
y = y.transform(np.log) I get MAE accuracy of around 2 (log-transformed units I suppose?), which is e^2 = 7.39 (y_raw). This is a significant drop from 306k to only 7.39 ($) (or am I getting it wrong?), so I am a bit suspicious about it.
So here are my questions: 1) Did I get it correct that the error rate drop from 306k to only 7.39 is real and is valid? 2) How do I make a predictions from there? If I feed a sample to my model, receive a log-transformed output, lets say it returned a prediction of y_log = 10. Do I then simply use an inverse of it by placing e^10 = 22,026.5 and will it be my final prediction?