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

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

[adrinjalali]

I’m quite in favor of one or more plots which investigate the threshold. I’d agree with @dayyass that the two types of plots discussed here are very different in nature and in what they convey. I personally understand the proposed plot in the OP much better than a precision recall curve, and that’s probably because I have never ended up dealing with precision recall curves much in my career, whereas I’ve worked on finding thresholds for my models in a few instances.

In terms of API, I’d be more happy with something MetricThresholdCurve which can accept different metrics (precision, recall, f1, …) and plot it against the thresholds. We can then use it 3 times to achieve what the OP suggests.

[glemaitre]

This would also motivate some example/analysis when introducing the meta-estimator developed in: https://github.com/scikit-learn/scikit-learn/pull/16525