feat: Add custom metrics

[auguste-probabl]

Change description

This PR makes it possible to register a custom metric in an EstimatorReport, so that it's shown along with the other default metrics in summarize:

from sklearn.datasets import load_breast_cancer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import make_scorer, mean_absolute_error
from skore import evaluate
X, y = load_breast_cancer(return_X_y=True)
classifier = LogisticRegression(max_iter=10_000)
report = evaluate(classifier, X, y, splitter=0.2, pos_label=1)
report.metrics.register(
    make_scorer(mean_absolute_error, response_method="predict")
)
report.metrics.summarize().frame()

                     LogisticRegression
Metric
Accuracy                       0.947368
Precision                      0.984127
Recall                         0.925373
ROC AUC                        0.993649
Brier score                    0.036154
Fit time (s)                   0.324200
Predict time (s)               0.000323
Mean Absolute Error            0.052632

Closes #2061

Contribution checklist

• Unit tests were added or updated (if necessary)
• Documentation was added or updated (if necessary)
• The documentation builds and renders properly (if it does, our bot will add a comment linking
  to a preview of the documentation to review it visually)
• A new changelog entry was added to CHANGELOG.rst (if necessary; typically, if your change
  requires updating tests)

AI usage disclosure

AI tools were involved for:

• Code generation (e.g., when writing an implementation or fixing a bug)
• Test/benchmark generation
• Documentation (including examples)
• Research and understanding

I had an LLM write down specs in docs/design, write some tests based on those specs, and some of the implementation.

[auguste-probabl]

This test was done for every report, so I moved it here