Why is accuracy not a good measure for classification problems?

Why is accuracy not a good measure for classification problems?

… in the framework of imbalanced data-sets, accuracy is no longer a proper measure, since it does not distinguish between the numbers of correctly classified examples of different classes. Hence, it may lead to erroneous conclusions …

Why the accuracy is not good metric to evaluate the classifier explain?

Accuracy can be a useful measure if we have the same amount of samples per class but if we have an imbalanced set of samples accuracy isn’t useful at all. Even more so, a test can have a high accuracy but actually perform worse than a test with a lower accuracy.

What changes can you make to the classification process to make it better?

But, some methods to enhance a classification accuracy, talking generally, are:

1. Cross Validation : Separe your train dataset in groups, always separe a group for prediction and change the groups in each execution.
2. Cross Dataset : The same as cross validation, but using different datasets.

When is classification accuracy for imbalanced class distributions wrong?

This means that intuitions for classification accuracy developed on balanced class distributions will be applied and will be wrong, misleading the practitioner into thinking that a model has good or even excellent performance when it, in fact, does not. Consider the case of an imbalanced dataset with a 1:100 class imbalance.

Is the impact of class imbalance in classification performance metrics?

For this reason, several metrics are usually considered, which permits the polyhedral characteristics of the classification performance to be viewed from different points of views. The impact of class imbalance on classification performance metrics has therefore become a major issue.

Why does the accuracy of a classification model fail?

Accuracy and error rate are the de facto standard metrics for summarizing the performance of classification models. Classification accuracy fails on classification problems with a skewed class distribution because of the intuitions developed by practitioners on datasets with an equal class distribution.

Why is imbalanced classification difficult in machine learning?

As such, the size of the dataset dramatically impacts the imbalanced classification task, and datasets that are thought large in general are, in fact, probably not large enough when working with an imbalanced classification problem. Without a sufficient large training set, a classifier may not generalize characteristics of the data.