Class-imbalanced dataset: Difference between revisions

Class imbalance is a frequent issue in machine learning, where one or more classes in a dataset have significantly fewer examples than others. This imbalance makes it difficult for machine learning algorithms to accurately predict the minority class, leading to biased and inaccurate models.

 

This article will define class-imbalanced datasets in machine learning, discuss its challenges, and present various techniques to address this issue.

Class imbalance presents machine learning algorithms with several challenges. One of the most pressing issues is biased modeling towards classes in the majority.

When training a machine learning algorithm on an unbalanced dataset, it often prioritizes the majority class during training. This is because there are more examples of members of the majority group and thus more opportunities to learn from them. As such, the algorithm could misclassify members of the minority group, leading to inaccurate predictions and inaccurate models.

Another challenge lies in evaluating models trained on class-imbalanced datasets. Traditional evaluation metrics, such as accuracy, can be misleading since they do not take into account the class imbalance. For instance, a model that always predicts the majority class may have high accuracy on an unbalanced dataset but it won't be useful for practical applications.

Data-level techniques refer to altering a dataset in order to achieve an even distribution across classes. Common data manipulation techniques include undersampling, oversampling and data augmentation. Undersampling involves taking examples from the majority class in order to achieve balance, while oversampling duplicates examples from minority classes. Data augmentation generates new examples by applying transformations such as rotation or scaling to existing examples.

Algorithmic techniques involve altering the machine learning algorithm to accommodate for class imbalance. Common techniques include cost-sensitive learning, threshold moving, and ensemble methods. Cost-sensitive learning involves assigning different costs to different classes during training in order to adjust for the imbalance; threshold moving alters the decision threshold of the model in order to increase sensitivity towards minority classes; and ensemble methods combine multiple models together in order to enhance performance for those in the minority group.

To help the computer become more knowledgeable about all things, we can use different tricks. One such trick is to show it more pictures of cats and birds so that it can learn about them too. Another useful tactic is to introduce new words or concepts slowly over time.