add sklearn.metrics Display class to plot Precision/Recall/F1 for probability thresholds

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Describe the workflow you want to enable

Working with binary classifiers I often, in addition to PR-curve and ROC-curve, need Precision / Recall / F1 (y-axis) for probability thresholds (x-axis).

Describe your proposed solution

import numpy as np
from sklearn.metrics import precision_recall_curve, PrecisionRecallF1Display

y_true = np.array([0, 0, 1, 1])
y_scores = np.array([0.1, 0.4, 0.35, 0.8])

precision, recall, thresholds = precision_recall_curve(y_true, y_scores)

display = PrecisionRecallF1Display(precision, recall, thresholds, plot_f1=True)
display.plot()

Describe alternatives you've considered, if relevant

No response

Additional context

No response

Start by reviewing the proposed sklearn.metrics PrecisionRecallF1Display API and the precision_recall_curve workflow shown in the issue. Resolve the intended plotting and threshold behavior before implementation, then define tests and documentation that demonstrate Precision, Recall, and F1 against probability thresholds.