from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier

X, y = make_classification()

X_train, X_test, y_train, y_test = train_test_split(X, y)

DecisionTreeClassifier(max_features='auto').fit(X_train, y_train)

---------------------------------------------------------------------------
InvalidParameterError                     Traceback (most recent call last)
Cell 2 line 5
      [1] X, y = make_classification()
      [3] X_train, X_test, y_train, y_test = train_test_split(X, y)
----> [5] DecisionTreeClassifier(max_features='auto').fit(X_train, y_train)

File c:\Users\\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\base.py:1145, in _fit_context.<locals>.decorator.<locals>.wrapper(estimator, *args, **kwargs)
   1140 partial_fit_and_fitted = (
   1141     fit_method.__name__ == "partial_fit" and _is_fitted(estimator)
   1142 )
   1144 if not global_skip_validation and not partial_fit_and_fitted:
-> 1145     estimator._validate_params()
   1147 with config_context(
   1148     skip_parameter_validation=(
   1149         prefer_skip_nested_validation or global_skip_validation
   1150     )
   1151 ):
   1152     return fit_method(estimator, *args, **kwargs)

File c:\Users\\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\base.py:638, in BaseEstimator._validate_params(self)
    630 def _validate_params(self):
    631     """Validate types and values of constructor parameters
    632 
    633     The expected type and values must be defined in the `_parameter_constraints`
   (...)
    636     accepted constraints.
    637     """
--> 638     validate_parameter_constraints(
    639         self._parameter_constraints,
    640         self.get_params(deep=False),
    641         caller_name=self.__class__.__name__,
    642     )

File c:\Users\\AppData\Local\Programs\Python\Python312\Lib\site-packages\sklearn\utils\_param_validation.py:96, in validate_parameter_constraints(parameter_constraints, params, caller_name)
     90 else:
     91     constraints_str = (
     92         f"{', '.join([str(c) for c in constraints[:-1]])} or"
     93         f" {constraints[-1]}"
     94     )
---> 96 raise InvalidParameterError(
     97     f"The {param_name!r} parameter of {caller_name} must be"
     98     f" {constraints_str}. Got {param_val!r} instead."
     99 )

InvalidParameterError: The 'max_features' parameter of DecisionTreeClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.