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.