I am trying to cross-validate my scikit-learn script by mean of Orange3, thus obtaining a nice visual representation.
Doing step by step, to keep it simple I just tried to cross-validate a simple linear regression.
Following is the simple script for linear regression in
lr=LinearRegression(fit_intercept=True,normalize=False,copy_X=True,n_jobs=1) lm=lr.fit(X_train, y_train) lmscore_train=lm.score(X_train,y_train) ## R2 = 0.6264021467338086 lmscore_test=lm.score(X_test,y_test) ## R2 = -12.344747578839215
Whilst I was expecting to get the same result, this is not the case.
In Orange, I get R2=-10.792 whilst in Python, I get R2=-12.344747578839215.
Train and test split are the same in both cases.
Do you have a clue why that is?