There are 3 different APIs for model evaluation:
1. Estimator score method: Estimator/model object has a ‘score()’ method that provides a default evaluation
2. Scoring parameter: Predefined scoring parameter that can be passed into cross_val_score() method
3. Metric function: Functions defined in the metrics module

Area Under ROC Curve(AUC) is an example of Scoring parameter API.
Area (1.0): Perfect prediction
Area (0.5): Good as random

Note:
– For the classification problem, we will use the Pima Indians onset of diabetes dataset.
– Estimator/Algorithm: Logistic Regression
– Cross-Validation Split: K-Fold (k=10)

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# import modules
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split

# read data file from github
# dataframe: pimaDf
gitFileURL = 'https://raw.githubusercontent.com/andrewgurung/data-repository/master/pima-indians-diabetes.data.csv'
cols = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
pimaDf = pd.read_csv(gitFileURL, names = cols)

# convert into numpy array for scikit-learn
pimaArr = pimaDf.values

# Let's split columns into the usual feature columns(X) and target column(Y)
# Y represents the target 'class' column whose value is either '0' or '1'
X = pimaArr[:, 0:8]
Y = pimaArr[:, 8]

# set k-fold count
folds = 10

# set seed to reproduce the same random data each time
seed = 7

# split data using KFold
kfold = KFold(n_splits=folds, random_state=seed)

# instantiate a classification model
model = LogisticRegression()

# set scoring parameter to 'roc_auc'
scoring = 'roc_auc'

# call cross_val_score() to run cross validation
resultArr = cross_val_score(model, X, Y, cv=kfold, scoring=scoring)

# calculate mean of scores for all folds
meanAccuracy = resultArr.mean() * 100

# calculate standard deviation of scores for all folds
stdAccuracy = resultArr.std() * 100

# display accuracy
print("Mean accuracy: %.3f%%, Standard deviation: %.3f%%" % (meanAccuracy, stdAccuracy))

Mean accuracy: 82.357%, Standard deviation: 4.084%