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

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

# Create logistic regression
logit = LogisticRegression()

# Cross-validate model using accuracy
cross_val_score(logit, features, target, scoring='accuracy')

# Cross-validate model using macro averaged F1 score
cross_val_score(logit, features, target, scoring='f1_macro')
