KFold - LogLoss in training set and validation set for Decision Tree I'm working on an binary classifier using decision tree. The question is asking for , using cross-validation with 10 splits, to calculate the LogLoss at the training set and validation set.
This is the code:
model = DecisionTreeClassifier(criterion='entropy', max_depth=None)
cv = KFold(n_splits=10, shuffle=False)

scores = cross_val_score(model, x, y, scoring='neg_log_loss', cv=cv, n_jobs=-1)
# force scores to be positive
scores = absolute(scores)
scores.mean()

This is the output [scores.mean()] :
3.1660909289719665

**> How can I calculate the training set and the validation of Kfold? And

this output, would be which one?**

The 4 Options are: 8 and 6 / 0 and 3 / 0 and 0 / LogLoss does not make sense here .
Thanks in advance

cross_val_score returns only the validation scores. So, the mean you calculated is the mean of validation splits/sets. To find the training score, you may use cross_validate, get 10 training scores and average them.
