advanced classification metrics

import numpy as np
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.metrics import matthews_corrcoef, cohen_kappa_score


# Load the Iris dataset
data = load_iris()
X, y = data.data, data.target

#Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

#Train a logistic regression model
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

#Make predictions
y_pred = model.predict(X_test)

# Calculate advanced classification metrics
mcc = matthews_corrcoef(y_test, y_pred)
kappa = cohen_kappa_score(y_test, y_pred)
print(f"MCC: {mcc:.2f}, nKappa: {kappa:.2f}")