[BUG]: default value for `restack` in `stack_models` should be aligned

### pycaret version checks

- [X] I have checked that this issue has not already been reported [here](https://github.com/pycaret/pycaret/issues).

- [X] I have confirmed this bug exists on the [latest version](https://github.com/pycaret/pycaret/releases) of pycaret.

- [X] I have confirmed this bug exists on the master branch of pycaret (pip install -U git+https://github.com/pycaret/pycaret.git@master).


### Issue Description

`stack_models` has a wrong ?! default value for `restack` here:

https://github.com/pycaret/pycaret/blob/7943536b943bb05ae716b7f643b40c27a5cf49bd/pycaret/regression/functional.py#L1430-L1443

for the OOP implementation the default is set to `False`:
https://github.com/pycaret/pycaret/blob/7943536b943bb05ae716b7f643b40c27a5cf49bd/pycaret/regression/oop.py#L1729-L1743

The documentation also describes False as the default:
https://github.com/pycaret/pycaret/blob/7943536b943bb05ae716b7f643b40c27a5cf49bd/pycaret/regression/functional.py#L1489-L1491
and
https://github.com/pycaret/pycaret/blob/7943536b943bb05ae716b7f643b40c27a5cf49bd/pycaret/regression/oop.py#L1789-L1791

### Reproducible Example

```python
NA
```


### Expected Behavior

- default value should be `False`

### Actual Results

```python-traceback
NA
```


### Installed Versions

<details>
Python 3.11.7 (main, Dec  5 2023, 18:55:16) [GCC 12.2.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from pycaret import show_versions; show_versions()

System:
    python: 3.11.7 (main, Dec  5 2023, 18:55:16) [GCC 12.2.0]
executable: /usr/local/bin/python
   machine: Linux-5.15.133.1-microsoft-standard-WSL2-x86_64-with-glibc2.36

PyCaret required dependencies:
                 pip: 23.2.1
          setuptools: 69.0.2
             pycaret: 3.2.1
             IPython: 8.19.0
          ipywidgets: 8.1.1
                tqdm: 4.66.1
               numpy: 1.25.2
              pandas: 2.0.3
              jinja2: 3.1.2
               scipy: 1.10.1
              joblib: 1.3.2
             sklearn: 1.2.2
                pyod: 1.1.2
            imblearn: 0.11.0
   category_encoders: 2.6.3
            lightgbm: 4.2.0
               numba: 0.58.1
            requests: 2.31.0
          matplotlib: 3.6.1
          scikitplot: 0.3.7
         yellowbrick: 1.5
              plotly: 5.18.0
    plotly-resampler: Not installed
             kaleido: 0.2.1
           schemdraw: 0.15
         statsmodels: 0.14.1
              sktime: 0.21.1
               tbats: 1.1.3
            pmdarima: 2.0.4
              psutil: 5.9.7
          markupsafe: 2.1.3
             pickle5: Not installed
         cloudpickle: 3.0.0
         deprecation: 2.1.0
              xxhash: 3.4.1
           wurlitzer: 3.0.3

PyCaret optional dependencies:
                shap: Not installed
           interpret: Not installed
                umap: Not installed
     ydata_profiling: Not installed
  explainerdashboard: Not installed
             autoviz: Not installed
           fairlearn: Not installed
          deepchecks: Not installed
             xgboost: 2.0.3
            catboost: 1.2.2
              kmodes: 0.12.2
             mlxtend: 0.23.0
       statsforecast: 1.5.0
        tune_sklearn: Not installed
                 ray: Not installed
            hyperopt: Not installed
              optuna: Not installed
               skopt: Not installed
              mlflow: 2.9.2
              gradio: 3.50.2
             fastapi: 0.108.0
             uvicorn: 0.25.0
              m2cgen: 0.10.0
           evidently: 0.2.8
               fugue: 0.8.7
           streamlit: Not installed
             prophet: Not installed
</details>


	def stack_models(
	estimator_list: list,
	meta_model=None,
	meta_model_fold: Optional[Union[int, Any]] = 5,
	fold: Optional[Union[int, Any]] = None,
	round: int = 4,
	restack: bool = True,
	choose_better: bool = False,
	optimize: str = "R2",
	fit_kwargs: Optional[dict] = None,
	groups: Optional[Union[str, Any]] = None,
	verbose: bool = True,
	return_train_score: bool = False,
	):

	def stack_models(
	self,
	estimator_list: list,
	meta_model=None,
	meta_model_fold: Optional[Union[int, Any]] = 5,
	fold: Optional[Union[int, Any]] = None,
	round: int = 4,
	restack: bool = False,
	choose_better: bool = False,
	optimize: str = "R2",
	fit_kwargs: Optional[dict] = None,
	groups: Optional[Union[str, Any]] = None,
	verbose: bool = True,
	return_train_score: bool = False,
	):

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[BUG]: default value for `restack` in `stack_models` should be aligned #3870

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	restack: bool, default = False
	When set to False, only the predictions of estimators will be used as
	training data for the ``meta_model``.

[BUG]: default value for restack in stack_models should be aligned #3870

Description

pycaret version checks

Issue Description

Reproducible Example

Expected Behavior

Actual Results

Installed Versions

Metadata

Metadata

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[BUG]: default value for `restack` in `stack_models` should be aligned #3870