sklearn RandomForestRegressor oob_score_ looks wrong? I am a newbie to datascience. I installed the Jupyter notebook and was trying to create model for the kaggle titanic dataset. Below is the code I wrote - 

from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import roc_auc_score
import pandas as pd
X = pd.read_csv("train.csv")
y = X["Survived"]
X["Age"].fillna(X.Age.mean(),inplace = True)
numeric_variables = list(X.dtypes[X.dtypes != "object"].index)
model = RandomForestRegressor(n_estimators=100, oob_score = True, random_state = 42)
model.fit(X[numeric_variables],y)
model.oob_score_


I am getting the oob_score_ output as 1.0 while the actual output as per tutorial should be 0.13420....
After that I did 

y_oob = model.oob_prediction_
print "c,stat:", roc_auc_score(y,y_oob)


The output of this as well I am getting 1.0. Is there something wrong in my code or setup of Jupyter?
You are using Random Forest Regressor, but since you have a binary response where a passenger survives or not, the Random Forest Classifier should be used.  If this doesn't fix the issue, please send the link to the tutorial.