What is the best metric for classification?

What is the best metric for classification?

Accuracy
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.

Which of the following is the most accurate metric for an imbalance classification problem?

The most commonly used ranking metric is the ROC Curve or ROC Analysis.

When do you need different metrics for imbalanced classification?

Importantly, different evaluation metrics are often required when working with imbalanced classification. Unlike standard evaluation metrics that treat all classes as equally important, imbalanced classification problems typically rate classification errors with the minority class as more important than those with the majority class.

Is it bad to use common metrics in imbalanced domains?

In fact, the use of common metrics in imbalanced domains can lead to sub-optimal classification models and might produce misleading conclusions since these measures are insensitive to skewed domains. — A Survey of Predictive Modelling under Imbalanced Distributions, 2015.

How is recall used to measure imbalanced classification?

Recall for Imbalanced Classification Recall is a metric that quantifies the number of correct positive predictions made out of all positive predictions that could have been made. Unlike precision that only comments on the correct positive predictions out of all positive predictions, recall provides an indication of missed positive predictions.

Which is the best metric to measure classification?

The model we are working with is basic logistic regression. Complete source for the following code snippets can be found at github. The very simple metric to measure classification is basic accuracy i.e. ratio of correct predictions to the total number of samples in dataset.