Snippet for multi-class classification problem using scikit-learn

snippet.py

from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.datasets import make_classification, load_iris
from sklearn.metrics import roc_auc_score, make_scorer
from sklearn import set_config

set_config(display='diagram')   
pipeline = make_pipeline(LogisticRegression(random_state=0))
pipeline

X, y = make_classification(n_samples=100, n_classes = 3, n_features=3, n_redundant=0, n_clusters_per_class=1)
cv = StratifiedKFold(n_splits=5)
cross_val_score(pipeline, X, y, cv=cv, scoring = make_scorer(roc_auc_score, multi_class='ovr', needs_proba=True))