correct, the AUC will be 1. And, if predictions are 0% correct, AUC will be 0.

We can use the following Python code to calculate the AUC for a classification problem.

from sklearn.model_selection import KFold
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
import pandas

data = pandas.read_csv("diabetes.csv")
D = data.values
X = D[:, :-1]
y = D[:, -1]

k_fold = KFold(n_splits=10, shuffle=True, random_state=1)
classifier = LogisticRegression(solver="liblinear")

results = cross_val_score(classifier, X, y, cv=k_fold, scoring="roc_auc")
mean_score = results.mean()
print("AUC: ", mean_score)

Here, we are first reading the Pima Indians Diabetes dataset using the pandas Python library. The dataset contains various predictor variables such as the number of pregnancies the patient has had, the BMI, insulin level, age, etc. A machine learning model can learn from the dataset and predict whether the patient has diabetes based on these predictor variables.

D = data.values
X = D[:, :-1]
y = D[:, -1]

Now, we are splitting the columns of the dataset into features and the target variable. X here contains all the features, and y contains the target variable.

k_fold = KFold(n_splits=10, shuffle=True, random_state=1)

We are now initializing the k-fold cross-validation. n_splits is the number of splits. The argument shuffle=True indicates that we are shuffling the data before splitting. And random_state is used to initialize the pseudo-random number generator that is used to shuffle the data.

classifier = LogisticRegression(solver="liblinear")

Now, we are initializing the classifier using the LogisticRegression class. Please note that LogisticRegression(), by default, uses libfgs or Limited-memory Broyden–Fletcher–Goldfarb–Shanno. This solver may be good for smaller datasets. On larger datasets, libfgs may fail to converge. So, we are here using the liblinear solver…