I have a dataset with 8 dependent variables (2 categorical data). I have applied ExtraTreeClassifier() to eliminate some of dependent variables. I had also feature scale the X,y .
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X = sc.fit_transform(X)
X = sc.transform(X)
y = sc.fit_transform(y)
y = sc.transform(y)
And after this I have split the dataset like
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X_new, encoded2,
test_size = 0.25, random_state = 0)
And now I am applying DecisionTreeRegressor algorithm for prediction. But I want to the actual prediction (right now I am getting scaled value). How to do that? Is there any other approach to do it? Because the way I have done is giving RMSE = 0.02 and if I am not feature scaling dependent variable RMSE = 18.4. Please suggest how to solve this kind of problem.