Contents
How are evaluation metrics used for classification of imbalanced data?
Evaluation measures play a crucial role in both assessing the classification performance and guiding the classifier modeling. — Classification Of Imbalanced Data: A Review, 2009. There are standard metrics that are widely used for evaluating classification predictive models, such as classification accuracy or classification error.
Can a high accuracy model be used for imbalanced classification?
Although widely used, classification accuracy is almost universally inappropriate for imbalanced classification. The reason is, a high accuracy (or low error) is achievable by a no skill model that only predicts the majority class. For more on the failure of classification accuracy, see the tutorial:
What happens if you choose the wrong metric for classification?
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.
How are three types of metrics for evaluating classifier models?
How there are three main types of metrics for evaluating classifier models, referred to as rank, threshold, and probability. How to choose a metric for imbalanced classification if you don’t know where to start.
When to use a lift chart in data analysis?
You can use this lift chart to verify that your model is doing what you expect from it. Let’s say there would be a spike in the lower scored groups; then you know right away that your model has some flaw, it doesn’t reflect the reality properly.
Which is the best classifier for imbalanced data?
PLoS ONE 12 (6): e0177678. https://doi.org/10.1371/journal.pone.0177678 Copyright: © 2017 Boughorbel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.