I'm trying to perform a regression on my data in order to predict an optimal delay for my products sales.
ID Delay(days) 50 120
I've used train_test_split to split my data, everything was fine. But R2 squared got like between 0.07 and -0.12.
My first question is how can I interpret this, improve it ?
My second step is to use the LeaveOneOut, Kfolds, so I went to the sklearn doc and grabbed the code there but I'm getting an error saying: IndexError: indices are out-of-bounds.
Can someone help me in explaining how this works in term of coding ?
EDIT: Here is my code: I have 2 datasets, train which has all sold and thrown products, and 'test' data that has all current products.
y = train['Delay'] X = train.drop('Delay',axis=1) loo = LeaveOneOut() for train_index, test_index in loo.split(X): print("TRAIN:", train_index, "TEST:", test_index) X_train, X_test = X[train_index], X[test_index] y_train, y_test = y[train_index], y[test_index] print(X_train, X_test, y_train, y_test) model = xgb.XGBRegressor() model.fit(X_train,y_train) score = model.score(X_test, y_test) ypredict = model.predict(test) print(score)