Which type of cross-validation is used for an imbalanced dataset?

For example, we can use a version of k-fold cross-validation that preserves the imbalanced class distribution in each fold. It is called stratified k-fold cross-validation and will enforce the class distribution in each split of the data to match the distribution in the complete training dataset.

How do you handle imbalanced data in classification?

Approach to deal with the imbalanced dataset problem

1. Choose Proper Evaluation Metric. The accuracy of a classifier is the total number of correct predictions by the classifier divided by the total number of predictions.
2. Resampling (Oversampling and Undersampling)
3. SMOTE.
4. BalancedBaggingClassifier.
5. Threshold moving.

What is imbalanced data classification?

Imbalanced classification refers to a classification predictive modeling problem where the number of examples in the training dataset for each class label is not balanced. That is, where the class distribution is not equal or close to equal, and is instead biased or skewed.

What is considered imbalanced data?

A classification data set with skewed class proportions is called imbalanced. Classes that make up a large proportion of the data set are called majority classes. Those that make up a smaller proportion are minority classes.

What is imbalanced data set?

Imbalanced data sets are a special case for classification problem where the class distribution is not uniform among the classes. Typically, they are composed by two classes: The majority (negative) class and the minority (positive) class.

What happens if data is imbalanced?

Imbalanced data typically refers to a classification problem where the number of observations per class is not equally distributed; often you’ll have a large amount of data/observations for one class (referred to as the majority class), and much fewer observations for one or more other classes (referred to as the …