LightGBM multiclassification code

python
import lightgbm as lgb
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

train_data = lgb.Dataset(X_train, label=y_train)

parameters = {
    'objective': 'multiclass',
    'num_class': 3,
    'metric': 'multi_logloss',
    'boosting_type': 'gbdt',
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9
}

lgb_model = lgb.train(parameters,
                      train_data,
                      valid_sets=[train_data],
                      num_boost_round=100,
                      early_stopping_rounds=10)

y_pred = lgb_model.predict(X_test)
y_pred = [list(x).index(max(x)) for x in y_pred]

accuracy = accuracy_score(y_test, y_pred)

print(f"Accuracy: {accuracy}")