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Precision-Recall-Gain Curves: PR Analysis Done Right

Precision-Recall analysis abounds in applications of binary classification where true negatives do not add value and hence should not affect assessment of the classifier's performance. Perhaps inspired by the many advantages of receiver operating characteristic (ROC) curves and the area under such curves for accuracy-based performance assessment, many researchers have taken to report Precision-Recall (PR) curves and associated areas as performance metric. We demonstrate in this paper that this practice is fraught with difficulties, mainly because of incoherent scale assumptions -- e.g., the area under a PR curve takes the arithmetic mean of precision values whereas the $F_{\beta}$ score applies the harmonic mean. We show how to fix this by plotting PR curves in a different coordinate system, and demonstrate that the new Precision-Recall-Gain curves inherit all key advantages of ROC curves. In particular, the area under Precision-Recall-Gain curves conveys an expected $F_1$ score on a harmonic scale, and the convex hull of a Precision-Recall-Gain curve allows us to calibrate the classifier's scores so as to determine, for each operating point on the convex hull, the interval of $\beta$ values for which the point optimises $F_{\beta}$. We demonstrate experimentally that the area under traditional PR curves can easily favour models with lower expected $F_1$ score than others, and so the use of Precision-Recall-Gain curves will result in better model selection.

ROC Curve and AUC -- Explained

ROC (receiver operating characteristics) curve and AOC (area under the curve) are performance measures that provide a comprehensive evaluation of classification models. AUC turns the ROC curve into a numeric representation of performance for a binary classifier. AUC is the area under the ROC curve and takes a value between 0 and 1. AUC indicates how successful a model is at separating positive and negative classes. Before going in detail, let's first explain the confusion matrix and how different threshold values change the outcome of it. A confusion matrix is not a metric to evaluate a model, but it provides insight into the predictions.

ROC-AUC Curve For Comprehensive Analysis Of Machine Learning Models

In machine learning when we build a model for classification tasks we do not build only a single model. We never rely on a single model since we have many different algorithms in machine learning that work differently on different datasets. We always have to build a model that best suits the respective data set so we try building different models and at last we choose the best performing model. For doing this comparison we cannot always rely on a metric like an accuracy score, the reason being for any imbalance data set the model will always predict the majority class. But it becomes important to check whether the positive class is predicted as the positive and negative class as negative by the model.

Understanding binary cross-entropy / log loss: a visual explanation

If you are training a binary classifier, chances are you are using binary cross-entropy / log loss as your loss function. Have you ever thought about what exactly does it mean to use this loss function? The thing is, given the ease of use of today's libraries and frameworks, it is very easy to overlook the true meaning of the loss function used. I was looking for a blog post that would explain the concepts behind binary cross-entropy / log loss in a visually clear and concise manner, so I could show it to my students at Data Science Retreat. Let's start with 10 random points: This is our only feature: x.

Understanding binary cross-entropy / log loss: a visual explanation

If you are training a binary classifier, chances are you are using binary cross-entropy / log loss as your loss function. Have you ever thought about what exactly does it mean to use this loss function? The thing is, given the ease of use of today's libraries and frameworks, it is very easy to overlook the true meaning of the loss function used. I was looking for a blog post that would explain the concepts behind binary cross-entropy / log loss in a visually clear and concise manner, so I could show it to my students at Data Science Retreat. Let's start with 10 random points: This is our only feature: x.