Why accuracy is not good for imbalanced dataset?

Why accuracy is not good for imbalanced dataset?

… 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 …

Which of these is an effective technique to measure your model performance on imbalanced dataset?

1. Random Undersampling and Oversampling. A widely adopted and perhaps the most straightforward method for dealing with highly imbalanced datasets is called resampling. It consists of removing samples from the majority class (under-sampling) and/or adding more examples from the minority class (over-sampling).

Which is better AUC or accuracy?

AUC is better measure of classifier performance than accuracy because it does not bias on size of test or evaluation data. Accuracy is always biased on size of test data. In most of the cases, we use 20\% data as evaluation or test data for our algorithm of total training data.

Which evaluation method is not good for unbalanced datasets?

The conventional model evaluation methods do not accurately measure model performance when faced with imbalanced datasets. Standard classifier algorithms like Decision Tree and Logistic Regression have a bias towards classes which have number of instances. They tend to only predict the majority class data.

How do you evaluate a classifier performance?

You simply measure the number of correct decisions your classifier makes, divide by the total number of test examples, and the result is the accuracy of your classifier. It’s that simple.

What are the ROC curves and precision-recall curves?

ROC Curves and Precision-Recall Curves provide a diagnostic tool for binary classification models. ROC AUC and Precision-Recall AUC provide scores that summarize the curves and can be used to compare classifiers. ROC Curves and ROC AUC can be optimistic on severely imbalanced classification problems with few samples of the minority class.

What is the ROC curve in machine learning?

The ROC curve is a mathematical curve and not an individual number statistic. In particular, this means that the comparison of two algorithms on a dataset does not always produce an apparent order. Accuracy (= 1 – error rate) is a standard method employed to estimate training algorithms.

What is the Receiver Operating Characteristic (ROC) curve?

The “Receiver Operating Characteristic” (ROC) curve is an alternative to Accuracy for evaluating learning algorithms on raw datasets. The ROC curve is a mathematical curve and not an individual number statistic. In particular, this means that the comparison of two algorithms on a dataset does not always produce an apparent order.

What is the difference between ROC AUC and ROC Accuracy?

The first big difference is that you calculate accuracy on the predicted classes while you calculate ROC AUC on predicted scores. That means you will have to find the optimal threshold for your problem. Moreover, accuracy looks at fractions of correctly assigned positive and negative classes.