Leave one out Cross validation using sklearn (Multiple CSV) I have 52 CSV files in a folder. I want to build a model based on this data. That's why I want to Leave one out cross-validation on these data. How can I do this using sci-kit learn in python?  

I tried from sci kit document and also search many resources.

import glob
import numpy as np
import pandas as pd
from sklearn.cross_validation import LeaveOneOut
path=r'...................\Data\New 
design process data'
filelist=glob.glob(path + "/*.csv")
loo=LeaveOneOut()
for train,test in loo.split(filelist):
   print("%s %s" % (train, test))


But it showed errors.

init() missing 1 required positional argument: 'n'

I am new in python as well as sci-kit learn. If anyone can help me, It would be a great convenience.
Leave One Out Cross Validation is just a special case of K- Fold Cross Validation where the number of folds =  the number of samples in the dataset you want to run cross validation on.

For Python , you can do as follows:

from sklearn.model_selection import cross_val_score
scores = cross_val_score(classifier , X = input data , y = target values , cv = X.shape[0]) 


Here , cv = the number of folds . As cv = number of samples here , we will get Leave One Out Cross Validation.
The length of list(scores) will be equal to number of samples in input data.