【PYTHON】metric confusion

 from pandas import read_csv

from sklearn.model_selection import train_test_split

from sklearn.metrics import confusion_matrix

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]

test_size = 0.33

seed = 7

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=test_size,random_state=seed)

model = LogisticRegression(solver='liblinear')

model.fit(X_train, Y_train)

predicted = model.predict(X_test)

matrix = confusion_matrix(Y_test, predicted)

print(matrix)