【PYTHON】Mean Estimated Accuracy Logistic Regression

 from pandas import read_csv

from sklearn.model_selection import KFold

from sklearn.model_selection import cross_val_score

from sklearn.linear_model import LogisticRegression

filename = 'pima-indians-diabetes.csv'

#url = 'https://myfilecsv.com/test.csv'

names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']

dataframe = read_csv(filename, names=names)

array = dataframe.values

#splitting the array to input and output

X = array[:,0:8]

Y = array[:,8]

num_folds = 10

seed = 7

kfold = KFold(n_splits = num_folds, random_state = seed)

model = LogisticRegression(solver='liblinear')

results = cross_val_score(model, X, Y, cv=kfold)

print("Mean Estimated Accuracy Logistic Regression: %f " % (results.mean()))