PrecisionRecallCurve

Evaluates the precision-recall trade-off for binary classification models and visualizes the Precision-Recall curve.

Purpose

The Precision Recall Curve metric is intended to evaluate the trade-off between precision and recall in classification models, particularly binary classification models. It assesses the model's capacity to produce accurate results (high precision), as well as its ability to capture a majority of all positive instances (high recall).

Test Mechanism

The test extracts ground truth labels and prediction probabilities from the model's test dataset. It applies the precision_recall_curve method from the sklearn metrics module to these extracted labels and predictions, which computes a precision-recall pair for each possible threshold. This calculation results in an array of precision and recall scores that can be plotted against each other to form the Precision-Recall Curve. This curve is then visually represented by using Plotly's scatter plot.

Signs of High Risk

A lower area under the Precision-Recall Curve signifies high risk.
This corresponds to a model yielding a high amount of false positives (low precision) and/or false negatives (low recall).
If the curve is closer to the bottom left of the plot, rather than being closer to the top right corner, it can be a sign of high risk.

Strengths

This metric aptly represents the balance between precision (minimizing false positives) and recall (minimizing false negatives), which is especially critical in scenarios where both values are significant.
Through the graphic representation, it enables an intuitive understanding of the model's performance across different threshold levels.

Limitations

This metric is only applicable to binary classification models - it raises errors for multiclass classification models or Foundation models.
It may not fully represent the overall accuracy of the model if the cost of false positives and false negatives are extremely different, or if the dataset is heavily imbalanced.