ValueError Traceback (most recent call last)
Cell In[12], line 28
24 model = make_pipeline(preprocessor, LinearRegression())
26 model.fit(iris, iris.sepal_length)
---> 28 pd = partial_dependence(estimator=model, X= iris, features= ["sepal_length"], categorical_features= [])

File ~.venv/lib/python3.10/site-packages/sklearn/utils/_param_validation.py:213, in validate_params..decorator..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 ~.venv/lib/python3.10/site-packages/sklearn/inspection/_partial_dependence.py:679, in partial_dependence(estimator, X, features, sample_weight, categorical_features, feature_names, response_method, percentiles, grid_resolution, method, kind)
675 is_categorical = [
676 idx in categorical_features_idx for idx in features_indices
677 ]
678 else:
--> 679 raise ValueError(
680 "Expected categorical_features to be an array-like of boolean,"
681 f" integer, or string. Got {categorical_features.dtype} instead."
682 )
684 grid, values = _grid_from_X(
685 _safe_indexing(X, features_indices, axis=1),
686 percentiles,
687 is_categorical,
688 grid_resolution,
689 )
691 if method == "brute":

ValueError: Expected categorical_features to be an array-like of boolean, integer, or string. Got float64 instead.