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Tour of Evaluation Metrics for Imbalanced Classification


A classifier is only as good as the metric used to evaluate it. If you choose the wrong metric to evaluate your models, you are likely to choose a poor model, or in the worst case, be misled about the expected performance of your model. Choosing an appropriate metric is challenging generally in applied machine learning, but is particularly difficult for imbalanced classification problems. Firstly, because most of the standard metrics that are widely used assume a balanced class distribution, and because typically not all classes, and therefore, not all prediction errors, are equal for imbalanced classification. In this tutorial, you will discover metrics that you can use for imbalanced classification. Tour of Evaluation Metrics for Imbalanced Classification Photo by Travis Wise, some rights reserved.

Model Evaluation Metrics in Machine Learning - KDnuggets


Predictive models have become a trusted advisor to many businesses and for a good reason. These models can "foresee the future", and there are many different methods available, meaning any industry can find one that fits their particular challenges. When we talk about predictive models, we are talking either about a regression model (continuous output) or a classification model (nominal or binary output). While data preparation and training a machine learning model is a key step in the machine learning pipeline, it's equally important to measure the performance of this trained model. How well the model generalizes on the unseen data is what defines adaptive vs non-adaptive machine learning models.

A critical analysis of metrics used for measuring progress in artificial intelligence Artificial Intelligence

Comparing model performances on benchmark datasets is an integral part of measuring and driving progress in artificial intelligence. A model's performance on a benchmark dataset is commonly assessed based on a single or a small set of performance metrics. While this enables quick comparisons, it may also entail the risk of inadequately reflecting model performance if the metric does not sufficiently cover all performance characteristics. Currently, it is unknown to what extent this might impact current benchmarking efforts. To address this question, we analysed the current landscape of performance metrics based on data covering 3867 machine learning model performance results from the web-based open platform 'Papers with Code'. Our results suggest that the large majority of metrics currently used to evaluate classification AI benchmark tasks have properties that may result in an inadequate reflection of a classifiers' performance, especially when used with imbalanced datasets. While alternative metrics that address problematic properties have been proposed, they are currently rarely applied as performance metrics in benchmarking tasks. Finally, we noticed that the reporting of metrics was partly inconsistent and partly unspecific, which may lead to ambiguities when comparing model performances.

Precision-Recall-Gain Curves: PR Analysis Done Right

Neural Information Processing Systems

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.