ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].

### Describe the bug

I will be succinct. I am training a binary classification dataset on "rain" or "not rain". This is a binary target. Yet scikit-learn throws an error stating that it's not binary. Is this expected behavior / what am I missing?

![samp](https://github.com/user-attachments/assets/f34d58e3-30e7-4c94-aea9-51325fbf76dc)


### Steps/Code to Reproduce

any dataset with a binary target variable

### Expected Results

No error is thrown

### Actual Results

```

0%|                                                    | 0/6 [00:28<?, ?it/s]
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[75], line 1
----> 1 final_dct = model_selection_kfold(model_lst, rain_ml_fin, "RainTomorrow_Yes")

Cell In[74], line 32, in model_selection_kfold(models, df, dep_var)
     29 scores_dct[str(model)]["model"] = model        
     30 scores_dct[str(model)]["preds"] = preds        
     31 scores_dct[model] = {
---> 32     'precision':metrics.precision_score(preds, y_test), 
     33     'recall':metrics.recall_score(preds, y_test), 
     34     'accuracy':metrics.accuracy_score(preds, y_test), 
     35     'f1':metrics.f1_score(preds, y_test),
     36     'train':clf.score(x_train, y_train),
     37     'test':clf.score(x_test, y_test),
     38     'cv':cv_score
     39 }
     41 print('\n')
     42 print('The model ', model, 'had the following Classification Report')

File [~\Languages\Lib\site-packages\sklearn\utils\_param_validation.py:213](http://localhost:8888/lab/tree/rainfall_aus/~/Languages/Lib/site-packages/sklearn/utils/_param_validation.py#line=212), in validate_params.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    207 try:
    208     with config_context(
    209         skip_parameter_validation=(
    210             prefer_skip_nested_validation or global_skip_validation
    211         )
    212     ):
--> 213         return func(*args, **kwargs)
    214 except InvalidParameterError as e:
    215     # When the function is just a wrapper around an estimator, we allow
    216     # the function to delegate validation to the estimator, but we replace
    217     # the name of the estimator by the name of the function in the error
    218     # message to avoid confusion.
    219     msg = re.sub(
    220         r"parameter of \w+ must be",
    221         f"parameter of {func.__qualname__} must be",
    222         str(e),
    223     )

File [~\Languages\Lib\site-packages\sklearn\metrics\_classification.py:2204](http://localhost:8888/lab/tree/rainfall_aus/~/Languages/Lib/site-packages/sklearn/metrics/_classification.py#line=2203), in precision_score(y_true, y_pred, labels, pos_label, average, sample_weight, zero_division)
   2037 @validate_params(
   2038     {
   2039         "y_true": ["array-like", "sparse matrix"],
   (...)
   2064     zero_division="warn",
   2065 ):
   2066     """Compute the precision.
   2067 
   2068     The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
   (...)
   2202     array([0.5, 1. , 1. ])
   2203     """
-> 2204     p, _, _, _ = precision_recall_fscore_support(
   2205         y_true,
   2206         y_pred,
   2207         labels=labels,
   2208         pos_label=pos_label,
   2209         average=average,
   2210         warn_for=("precision",),
   2211         sample_weight=sample_weight,
   2212         zero_division=zero_division,
   2213     )
   2214     return p

File [~\Languages\Lib\site-packages\sklearn\utils\_param_validation.py:186](http://localhost:8888/lab/tree/rainfall_aus/~/Languages/Lib/site-packages/sklearn/utils/_param_validation.py#line=185), in validate_params.<locals>.decorator.<locals>.wrapper(*args, **kwargs)
    184 global_skip_validation = get_config()["skip_parameter_validation"]
    185 if global_skip_validation:
--> 186     return func(*args, **kwargs)
    188 func_sig = signature(func)
    190 # Map *args/**kwargs to the function signature

File [~\Languages\Lib\site-packages\sklearn\metrics\_classification.py:1789](http://localhost:8888/lab/tree/rainfall_aus/~/Languages/Lib/site-packages/sklearn/metrics/_classification.py#line=1788), in precision_recall_fscore_support(y_true, y_pred, beta, labels, pos_label, average, warn_for, sample_weight, zero_division)
   1626 """Compute precision, recall, F-measure and support for each class.
   1627 
   1628 The precision is the ratio ``tp / (tp + fp)`` where ``tp`` is the number of
   (...)
   1786  array([2, 2, 2]))
   1787 """
   1788 _check_zero_division(zero_division)
-> 1789 labels = _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
   1791 # Calculate tp_sum, pred_sum, true_sum ###
   1792 samplewise = average == "samples"

File [~\Languages\Lib\site-packages\sklearn\metrics\_classification.py:1578](http://localhost:8888/lab/tree/rainfall_aus/~/Languages/Lib/site-packages/sklearn/metrics/_classification.py#line=1577), in _check_set_wise_labels(y_true, y_pred, average, labels, pos_label)
   1576         if y_type == "multiclass":
   1577             average_options.remove("samples")
-> 1578         raise ValueError(
   1579             "Target is %s but average='binary'. Please "
   1580             "choose another average setting, one of %r." % (y_type, average_options)
   1581         )
   1582 elif pos_label not in (None, 1):
   1583     warnings.warn(
   1584         "Note that pos_label (set to %r) is ignored when "
   1585         "average != 'binary' (got %r). You may use "
   (...)
   1588         UserWarning,
   1589     )

ValueError: Target is multiclass but average='binary'. Please choose another average setting, one of [None, 'micro', 'macro', 'weighted'].
```

### Versions

```shell
System:
    python: 3.12.6 (tags/v3.12.6:a4a2d2b, Sep  6 2024, 20:11:23) [MSC v.1940 64 bit (AMD64)]
executable: C:\Users\benno\Languages\python.exe
   machine: Windows-10-10.0.19045-SP0

Python dependencies:
      sklearn: 1.5.2
          pip: 24.2
   setuptools: 75.1.0
        numpy: 2.0.2
        scipy: 1.14.1
       Cython: None
       pandas: 2.2.3
   matplotlib: 3.9.2
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 4
         prefix: libscipy_openblas
       filepath: C:\Users\benno\Languages\Lib\site-packages\numpy.libs\libscipy_openblas64_-caad452230ae4ddb57899b8b3a33c55c.dll
        version: 0.3.27
threading_layer: pthreads
   architecture: Haswell

       user_api: openmp
   internal_api: openmp
    num_threads: 4
         prefix: vcomp
       filepath: C:\Users\benno\Languages\Lib\site-packages\sklearn\.libs\vcomp140.dll
        version: None

       user_api: blas
   internal_api: openblas
    num_threads: 4
         prefix: libscipy_openblas
       filepath: C:\Users\benno\Languages\Lib\site-packages\scipy.libs\libscipy_openblas-5b1ec8b915dfb81d11cebc0788069d2d.dll
        version: 0.3.27.dev
threading_layer: pthreads
   architecture: Haswell
```

COMMENT:
Wait, I am fairly sure I've used accuracy as a scoring metric for binary classification. As I understand it, it's less complex than precision or recall and is just correct vs incorrect. Are you positive accuracy only works with multi-class / multi-label?

Labels: Bug, Needs Triage