I'm trying to learn scikit-learn and Machine Learning by using the Boston Housing Data Set.
# I splitted the initial dataset ('housing_X' and 'housing_y')
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(housing_X, housing_y, test_size=0.25, random_state=33)
# I scaled those two datasets
from sklearn.preprocessing import StandardScaler
scalerX = StandardScaler().fit(X_train)
scalery = StandardScaler().fit(y_train)
X_train = scalerX.transform(X_train)
y_train = scalery.transform(y_train)
X_test = scalerX.transform(X_test)
y_test = scalery.transform(y_test)
# I created the model
from sklearn import linear_model
clf_sgd = linear_model.SGDRegressor(loss='squared_loss', penalty=None, random_state=42)
Based on this new model clf_sgd, I am trying to predict the y based on the first instance of X_train.
X_new_scaled = X_train
y_new = clf_sgd.predict(X_new_scaled)
However, the result is quite odd for me (1.34032174, instead of 20-30, the range of the price of the houses)
I guess that this 1.34032174 value should be scaled back, but I am trying to figure out how to do it with no success. Any tip is welcome. Thank you very much.