[Bug] Found unknown categories

Estou realizando um treinamento do modelo com base no código abaixo:

# definindo parâmetros dos modelos
models = [
    (
        "PER", #Nome do modelo (abreviado)
        Perceptron(max_iter=1000), # Chamada do método do modelo
        {"penalty": ['l2','l1','elasticnet']} # Diferentes parametros que serão testados
    ),
    (
        "NB",
        BernoulliNB(),
        {"alpha": [1e-3, 0.5, 1]}
    ),
    (
        "DT",
        DecisionTreeClassifier(),
        {
            'criterion': ['gini','entropy'],
            'max_depth': [2,3,5,10,20],
            'min_samples_leaf': [5,10,20,50,100,200]
        }
    ),   
    (
        "RF",
        RandomForestClassifier(),
        {
            'criterion': ['gini','entropy'],
            'max_depth': [2,3,5,10,20],
            'min_samples_leaf': [5,10,20,50,100,200],
            'n_estimators': [10,25,30,50,100,200]
        }
    ),     
    (
        "LR", #Nome do modelo (abreviado)
        LogisticRegression(solver='saga', max_iter=1000), # Chamada do método do modelo
        {"penalty": ['none', 'l1', 'l2']} # Diferentes parametros que serão testados
    ),
    (
        "KNN",
        KNeighborsClassifier(metric='euclidean'),
        {"n_neighbors": np.arange(1, 31, 2), 'weights': ["uniform", "distance"]}
    ),
    (
        "XGB",
        XGBClassifier(),
        {
            'min_child_weight': [1, 5, 10],
            'gamma': [0.5, 1, 1.5, 2, 5],
            'subsample': [0.6, 0.8, 1.0],
            'colsample_bytree': [0.6, 0.8, 1.0],
            'max_depth': [3, 4, 5],
            'n_iter': [3,4,5],
            'learning_rate':[0.01, 0.05,0.1,0.5,1]
        }
    ),    
    (
        "SVM",
        SVC(max_iter=10000),
        {'C':[1, 10, 100, 1000],'gamma':[1, 0.1, 0.001, 0.0001], 'kernel':['linear', 'poly', 'rbf', 'sigmoid', 'precomputed']}
    ),
    (
        "MLP",
        MLPClassifier(),
        {
            'hidden_layer_sizes': [(5,10,15,20,25,30),(5,10,15,20,25,30)],
            'activation': ['identity', 'logistic', 'tanh', 'relu'],
            'solver': ['lbfgs', 'sgd', 'adam'],
            'alpha': [0.0001, 0.01, 0.05,0.1,0.5,1],
            'learning_rate': ['constant','adaptive'],
}
    ),
]

metrics = {
            'accuracy': make_scorer(accuracy_score, average='weighted'),
            'precision': make_scorer(precision_score, average='weighted', zero_division=1),
            'recall': make_scorer(recall_score, average='weighted', zero_division=1),
            'f1': make_scorer(f1_score, average='weighted', zero_division=1)
        }

X = df.drop(columns=[target_column], axis=1)
y = (df[[target_column]].to_numpy().ravel())

cv = ShuffleSplit(n_splits=30, train_size=0.8, random_state=42) #Separa em conjuntos de teste e treino

# Realizando treinamento dos modelos selecionados
results = {}
for model_name, model, model_params in models:
    print(f'{model_name} run...')

    model_gs = GridSearchCV(model, model_params, scoring='accuracy')
    approach = Pipeline([
        ("preprocessing", preprocessing),
        ("model", model_gs)
    ])
    model_results = cross_validate(
        approach,
        X=X,
        y=y,
        scoring=metrics,
        cv=cv,
        n_jobs=-1,
        return_train_score=False,
        error_score=0
    )
    model_results['name'] = [model_name] * len(model_results['score_time'])
    if results:
        for key, value in model_results.items():
            results[key] = np.append(results[key], value)
    else:
        results = model_results

e para cada modelo treinado surge duas mensagens de warnings:

a primeira é:

UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to 0.  KeyError: 'predict'

A segunda é:

ValueError: Found unknown categories

O que posso fazer para resolver esses erros?