[SOLVED] difference between cross_val

difference between cross_val_score and KFold

I am learning Machine learning and I am having this doubt. Can anyone tell me what is the difference between:-

from sklearn.model_selection import cross_val_score

and

from sklearn.model_selection import KFold

I think both are used for k fold cross validation, but I am not sure why to use two different code for same function. If there is something I am missing please do let me know. ( If possible please explain difference between these two methods)

Thanks,

Solution

cross_val_score is a function which evaluates a data and returns the score.
On the other hand, KFold is a class, which lets you to split your data to K folds.

So, these are completely different. Yo can make K fold of data and use it on cross validation like this:

# create a splitter object
kfold = KFold(n_splits = 10)       
# define your model (any model)
model = XGBRegressor(**params)     
# pass your model and KFold object to cross_val_score
# to fit and get the mse of each fold of data
cv_score = cross_val_score(model,
                           X, y, 
                           cv=kfold, 
                           scoring='neg_root_mean_squared_error')
print(cv_score.mean(), cv_score.std())