DOC: clarify the documentation for the loss functions used in GBRT, and Absolute Error in particular.

### Describe the bug

From my understanding, currently there is no way to minimize the MAE (Mean Absolute Error). Quantile regression with quantile=0.5 will optimize for the Median Absolute Error. This would be different from optimizing the MAE when the conditional distribution of the response variable is not symmetrically-distributed.

https://github.com/scikit-learn/scikit-learn/blob/46a7c9a5e4fe88dfdfd371bf36477f03498a3390/sklearn/_loss/loss.py#L574-L577

**What I expect**
- Using `HistGradientBoostingRegressor(loss="absolute_error")` should optimize for the mean of absolute errors.
- Using `HistGradientBoostingRegressor(loss="quantile", quantile=0.5)` should optimize for the median of absolute errors.

```python
        if sample_weight is None:
            return np.mean(y_true, axis=0)
        else:
            return _weighted_mean(y_true, sample_weight)
```

**What happens**
Both give the same results
- Using `HistGradientBoostingRegressor(loss="absolute_error")` optimizes for the median of absolute errors
- Using `HistGradientBoostingRegressor(loss="quantile", quantile=0.5)` optimizes for the median of absolute errors

**Suggested Actions**

If this is intended behavior:
- Feel free to close this issue marked as resolved.
- Kindly add a note in the documentation that "Absolute Error optimizes for Median Absolute Error, not Mean Absolute Error" as "absolute_error" is not very clear.
- I would appreciate if there was more explanation regarding on using custom loss functions #21614. This way, we could optimize for Mean Absolute Error, Median Absolute Error, Log Cosh, etc. as per the requirement.

**Note**
I have tried my best to go through the documentation prior to creating this issue. I am a fresh graduate in Computer Science, and if you believe this issue is not well-framed due to a misunderstanding of my concepts, kindly advise me and I'll work on it.

### Steps/Code to Reproduce

```python
# Imports
from sklearn.ensemble import HistGradientBoostingRegressor
import numpy as np

# Dataset Generation
x = np.linspace(start=0, stop=10, num=100)

n_repeat = 100 # no of x for each x
X = np.repeat(x, n_repeat)[:, np.newaxis]
y_true_mean = 1 * np.repeat(x, n_repeat)
noise = np.random.RandomState(0).lognormal(mean=0, sigma=1, size=y_true_mean.shape[0])
y_noisy = y_true_mean + noise

# Model Creation
mae = HistGradientBoostingRegressor(loss="absolute_error") # should be mean of absolute errors
quantile = HistGradientBoostingRegressor(loss="quantile", quantile=0.5) # should be median of absolute errors

# Fit & Prediction
y_pred_mae = mae.fit(X, y_noisy).predict(X)
y_pred_quantile = quantile.fit(X, y_noisy).predict(X)

# Prediction Comparison
print((y_pred_mae - y_pred_quantile).sum()) # both give same results
```

### Expected Results

Median and mean of absolute errors should give different results for a log-normally distributed response. Hence, the predictions should be different from each other, and the difference of their predictions, should total as a non-zero value.

### Actual Results

Predictions by both models are the same, which can be seen in the difference of their predictions, totaling as 0.

```shell
0.
```

### Versions

```shell
System:
    python: 3.10.12 (main, Nov  6 2024, 20:22:13) [GCC 11.4.0]
executable: /usr/bin/python3
   machine: Linux-6.1.85+-x86_64-with-glibc2.35

Python dependencies:
      sklearn: 1.5.2
          pip: 24.1.2
   setuptools: 75.1.0
        numpy: 1.26.4
        scipy: 1.13.1
       Cython: 3.0.11
       pandas: 2.2.2
   matplotlib: 3.8.0
       joblib: 1.4.2
threadpoolctl: 3.5.0

Built with OpenMP: True

threadpoolctl info:
       user_api: blas
   internal_api: openblas
    num_threads: 2
         prefix: libopenblas
       filepath: /usr/local/lib/python3.10/dist-packages/numpy.libs/libopenblas64_p-r0-0cf96a72.3.23.dev.so
        version: 0.3.23.dev
threading_layer: pthreads
   architecture: Haswell

       user_api: blas
   internal_api: openblas
    num_threads: 2
         prefix: libopenblas
       filepath: /usr/local/lib/python3.10/dist-packages/scipy.libs/libopenblasp-r0-01191904.3.27.so
        version: 0.3.27
threading_layer: pthreads
   architecture: Haswell

       user_api: openmp
   internal_api: openmp
    num_threads: 2
         prefix: libgomp
       filepath: /usr/local/lib/python3.10/dist-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0
        version: None
```

COMMENT:
> Please point us to the specific part of the documentation that was not clear and suggest a way to improve it. Otherwise I think we can close.

**Problem**
The term "squared_error" and "absolute_error" is not very clear: ie, is it Mean Squared  Error or Median Squared Error; Mean Absolute Error or Median Absolute Error. The documentation should make it clear that it is Mean Squared Error and Mean Absolute Error.

**Specific part of the documentation**
https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html

> loss{‘squared_error’, ‘absolute_error’, ‘gamma’, ‘poisson’, ‘quantile’}, default=’squared_error’
> The loss function to use in the boosting process. Note that the “squared error”, “gamma” and “poisson” losses actually implement “half least squares loss”, “half gamma deviance” and “half poisson deviance” to simplify the computation of the gradient. Furthermore, “gamma” and “poisson” losses internally use a log-link, “gamma” requires y > 0 and “poisson” requires y >= 0. “quantile” uses the pinball loss.

**Suggestion**
> The loss function to use in the boosting process. Note that the “squared error”, “gamma” and “poisson” losses actually implement “half least squares loss”, “half gamma deviance” and “half poisson deviance” to simplify the computation of the gradient. Furthermore, “gamma” and “poisson” losses internally use a log-link, “gamma” requires y > 0 and “poisson” requires y >= 0. “quantile” uses the pinball loss. **“absolute_error” refers to Mean Absolute Error; for other variants of absolute error, you need to write your own loss function**, **“squared_error” refers to Mean Squared Error; for other variants of squared error, you need to write your own loss function**

Labels: Documentation