I am new to the whole ML scene and am trying to resolve the Allstate Kaggle challenge to get a better feeling for the Random Forest Regression technique.
The challenge is evaluated based on the MAE for each row.
I've run the
RandomForrestRegressor on my validation set, using the
criterion=mae attribute. To my understanding this will run the Forest algorithm calculating the
mae instead of the
mse for each node.
After that I've used this:
metrics.mean_absolute_error(Y_valid, m.predict(X_valid)) in order to calculate the MAE for each row of data.
What I would like to know is if the logic I'm following is sound. Am I making a fundamental mistake or missing something here? Should I have used the default MSE based Regressor and then calculate the MAE of each row using the