I'm trying to build a simple model to predict the price of a cab ride, using features
such as hour, source, destination, car model, distance, and
weather features such as pressure and humidity.
I've encoded the unique values in source, destination and car model using
one hot encoding,
unique values in source and destination - 12 each
unique values in car model feature - 6.
sample size of data set - 300,000.
I've scaled the numeric data using Standard scaler.
I've tried Linear regression, Ridge, LASSO, Elastic net and Adaboot algorithm.
Results in RMSE:
Linear reg - 2.36
Ridge - 2.36
LASSO - 5.7
Elastic net - 6.3
Adaboost - 5.6
my doubt is why, Ridge, LASSO, Elastic Net and Adaboost doesn't perform well than Linear regression, and what might be the reason?
I just used different algorithms, just to understand how they perform in this data set (for academic purposes). I know, its difficult to comment, without examining the data.
Any comments or suggestions is appreciated.