I have a simple binary classification experiment I am trying to perform. Here is the code

from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import LinearSVC
from sklearn.model_selection import cross_val_score
import xgboost as xgb
models = [
    RandomForestClassifier(n_estimators=100, max_depth=3, random_state=0),
    LinearSVC(),
    MultinomialNB(),
    LogisticRegression(random_state=0),
    xgb.XGBClassifier(max_depth=3, 
                      objective='binary:logistic', 
                      n_estimators=100, 
                      num_classes=2, 
                      n_jobs = -1)
]
CV = 5
cv_df = pd.DataFrame(index=range(CV * len(models)))
entries = []
for model in models:
    model_name = model.__class__.__name__
    f1_score = cross_val_score(model, X_prep, labels, scoring = make_scorer(f1_score, average='weighted', labels=[2]), cv=CV)
    for fold_idx, f1score in enumerate(f1_score):
        entries.append((model_name, fold_idx, f1score))
cv_df = pd.DataFrame(entries, columns=['model_name', 'fold_idx', 'f1score'])

I am getting the following error message:

---------------------------------------------------------------------------
InvalidParameterError                     Traceback (most recent call last)
Cell In[34], line 22
     20 for model in models:
     21     model_name = model.__class__.__name__
---> 22     f1_score = cross_val_score(model, X_prep, labels, scoring = make_scorer(f1_score, average='weighted', labels=[2]), cv=CV)
     23     for fold_idx, f1score in enumerate(f1_score):
     24         entries.append((model_name, fold_idx, f1score))

File ~/anaconda3/envs/gpt-ds/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:203, in validate_params.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    200 to_ignore += ["self", "cls"]
    201 params = {k: v for k, v in params.arguments.items() if k not in to_ignore}
--> 203 validate_parameter_constraints(
    204     parameter_constraints, params, caller_name=func.__qualname__
    205 )
    207 try:
    208     with config_context(
    209         skip_parameter_validation=(
    210             prefer_skip_nested_validation or global_skip_validation
    211         )
    212     ):

File ~/anaconda3/envs/gpt-ds/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:95, in validate_parameter_constraints(parameter_constraints, params, caller_name)
     89 else:
     90     constraints_str = (
     91         f"{', '.join([str(c) for c in constraints[:-1]])} or"
     92         f" {constraints[-1]}"
     93     )
---> 95 raise InvalidParameterError(
     96     f"The {param_name!r} parameter of {caller_name} must be"
     97     f" {constraints_str}. Got {param_val!r} instead."
     98 )

InvalidParameterError: The 'score_func' parameter of make_scorer must be a callable. Got array([0., 0., 0., 0., 0.]) instead.

I am just trying to get the mac_avg f1-score for the cross_val_score. Any suggestions are appreciated, I tried some solutions that were relevant but this one in particular did not work for me.