Add missing value support for AdaBoost?

### Describe the bug

I am working on classifying samples in various datasets using the AdaBoostClassifier with the DecisionTreeClassifier as the base estimator. 

The DecisionTreeClassifier can handle np.nan values, so I assumed the AdaBoostClassifier would be able to as well.

However, that does not seem to be the case, as AdaBoost gives the error `ValueError: Input X contains NaN` when I try to use it with data containing NAs.

I asked if this was the intended behavior [here](https://github.com/scikit-learn/scikit-learn/discussions/30217) but have yet to receive a response.

If this isn't intentional, could AdaBoostClassifier be updated to support missing values when the base estimator does?

### Steps/Code to Reproduce

```
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import AdaBoostClassifier
import numpy as np


iris = load_iris()

# Set first position to nan
iris.data[0,0] = np.nan

# Confirm DecisionTreeClassifier still works
clf_works = DecisionTreeClassifier(max_depth=1)
cross_val_score(clf_works, iris.data, iris.target, cv=3)

# Explicitly call DecisionTreeClassifier as the base estimator
clf = AdaBoostClassifier(random_state=0, estimator=DecisionTreeClassifier(max_depth=1)) 

# Attempt to use AdaBoostClassifier w/ data containing nan
cross_val_score(clf, iris.data, iris.target, cv=3)
```

### Expected Results

No error is thrown when `DecisionTreeClassifier(max_depth=1)` is used as the classifier since the DecisionTreeClassifier can handle np.nan values.

Because of that, I expected AdaBoost to fit and train successfully too.

### Actual Results

```
C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\model_selection\_validation.py:976: UserWarning: Scoring failed. The score on this train-test partition for these parameters will be set to nan. Details: 
Traceback (most recent call last):
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\metrics\_scorer.py", line 144, in __call__
    score = scorer(estimator, *args, **routed_params.get(name).score)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\metrics\_scorer.py", line 472, in __call__
    return estimator.score(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\base.py", line 513, in score
    return accuracy_score(y, self.predict(X), sample_weight=sample_weight)
                             ^^^^^^^^^^^^^^^
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 628, in predict
    pred = self.decision_function(X)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 689, in decision_function
    X = self._check_X(X)
        ^^^^^^^^^^^^^^^^
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 94, in _check_X
    return validate_data(
           ^^^^^^^^^^^^^^
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\utils\validation.py", line 2933, in validate_data
    out = check_array(X, input_name="X", **check_params)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\utils\validation.py", line 1106, in check_array
    _assert_all_finite(
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\utils\validation.py", line 120, in _assert_all_finite
    _assert_all_finite_element_wise(
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\utils\validation.py", line 169, in _assert_all_finite_element_wise
    raise ValueError(msg_err)
ValueError: Input X contains NaN.
AdaBoostClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values

  warnings.warn(
C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\model_selection\_validation.py:526: FitFailedWarning: 
2 fits failed out of a total of 3.
The score on these train-test partitions for these parameters will be set to nan.
If these failures are not expected, you can try to debug them by setting error_score='raise'.

Below are more details about the failures:
--------------------------------------------------------------------------------
2 fits failed with the following error:
Traceback (most recent call last):
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\model_selection\_validation.py", line 864, in _fit_and_score
    estimator.fit(X_train, y_train, **fit_params)
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\base.py", line 1330, in wrapper
    return fit_method(estimator, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\ensemble\_weight_boosting.py", line 130, in fit
    X, y = validate_data(
           ^^^^^^^^^^^^^^
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\utils\validation.py", line 2950, in validate_data
    X, y = check_X_y(X, y, **check_params)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\utils\validation.py", line 1369, in check_X_y
    X = check_array(
        ^^^^^^^^^^^^
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\utils\validation.py", line 1106, in check_array
    _assert_all_finite(
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\utils\validation.py", line 120, in _assert_all_finite
    _assert_all_finite_element_wise(
  File "C:\Users\pacea\miniconda3\envs\jupyter\Lib\site-packages\sklearn\utils\validation.py", line 169, in _assert_all_finite_element_wise
    raise ValueError(msg_err)
ValueError: Input X contains NaN.
AdaBoostClassifier does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values

  warnings.warn(some_fits_failed_message, FitFailedWarning)
```

```
array([nan, nan, nan])
```

### Versions

```shell
System:
    python: 3.12.5 | packaged by conda-forge | (main, Aug  8 2024, 18:24:51) [MSC v.1940 64 bit (AMD64)]
executable: C:\Users\pacea\miniconda3\envs\jupyter\python.exe
   machine: Windows-10-10.0.19045-SP0

Python dependencies:
      sklearn: 1.6.0rc1
          pip: 24.2
   setuptools: 74.1.2
        numpy: 1.26.4
        scipy: 1.14.1
       Cython: None
       pandas: 2.2.2
   matplotlib: 3.9.2
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: mkl
    num_threads: 8
         prefix: libblas
       filepath: C:\Users\pacea\miniconda3\envs\jupyter\Library\bin\libblas.dll
        version: 2024.1-Product
threading_layer: intel

       user_api: openmp
   internal_api: openmp
    num_threads: 16
         prefix: vcomp
       filepath: C:\Users\pacea\miniconda3\envs\jupyter\vcomp140.dll
        version: None
```

COMMENT:
Great to hear that you managed to get it to work for your use case. In my opinion, hackability 
is an underrated aspect of scikit-learn: people find their ways and are able to tweak some parts to explore ideas. That doesn't mean that every possible variations should go into the library because we need to balance features with maintainability.

I would expect that in your benchmarks `HistGradientBoosting` overperforms `AdaBoost`, both in terms of generalization performance and computational efficiency (time taken by `fit` or `predict`).

Labels: New Feature