Key Issues Associated with Classification Accuracy

摘要： In this blog, we will unfold the key problems associated with classification accuracies, such as imbalanced classes, overfitting, and data bias, and proven ways to address those issues successfully.

Imbalanced Classes

The accuracy may be deceptive if the dataset contains classifications that are uneven. For instance, a model that merely predicts the majority class will be 99% accurate if the dominant class comprises 99% of the data. Unfortunately, it will not be able to appropriately classify the minority class. Other metrics including precision, recall, and F1-score should be used to address this issue.

The 5 most common techniques that can be used to address the problem of imbalanced class in classification accuracy are:

轉貼自： kdnuggets.com

若喜歡本文，請關注我們的臉書 Please Like our Facebook Page： Big Data In Finance