f1 score

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 f1_score

#Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df, y, test_size=0.1, 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)

# Metric
f1 = f1_score(y_test, y_pred,average='micro')
print(f1)