## Preliminaries

# Load libraries
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification

## Generate Features And Target Data

# Generate features matrix and target vector
X, y = make_classification(n_samples = 10000,
n_features = 3,
n_informative = 3,
n_redundant = 0,
n_classes = 2,
random_state = 1)

## Create Logistic Regression

# Create logistic regression
logit = LogisticRegression()

## Cross-Validate Model Using Accuracy

# Cross-validate model using accuracy
cross_val_score(logit, X, y, scoring="accuracy")

array([ 0.95170966, 0.9580084 , 0.95558223])