Contents
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.
What’s the difference between regression and error metrics?
The pool of error metrics you can choose from is different between classification and regression. In the latter you try to predict one continuous value, and with classification you predict discrete classes such as “healthy” or “not healthy”.
How are evaluation metrics used in predictive models?
When we talk about predictive models, we are talking either about a regression model (continuous output) or a classification model (nominal or binary output). The evaluation metrics used in each of these models are different. In classification problems, we use two types of algorithms (dependent on the kind of output it creates):
What are the threshold metrics for binary classification?
Majority Class: Negative outcome, class 0. Minority Class: Positive outcome, class 1. Most threshold metrics can be best understood by the terms used in a confusion matrix for a binary (two-class) classification problem.
Are there standard metrics for evaluating classification predictive models?
There are standard metrics that are widely used for evaluating classification predictive models, such as classification accuracy or classification error. Standard metrics work well on most problems, which is why they are widely adopted. But all metrics make assumptions about the problem or about what is important in the problem.
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:
Which is the most important metric in classification?
Accuracy, Precision, and Recall: Accuracy is the quintessential classification metric. It is pretty easy to understand. And easily suited for binary as well as a multiclass classification problem. Accuracy is the proportion of true results among the total number of cases examined.