Machine learning techniques often fail or give misleadingly optimistic performance on classification datasets with an imbalanced class distribution. The reason is that many machine learning... Read more

Resampling methods are designed to add or remove examples from the training dataset in order to change the class distribution. Once the class distributions are more balanced, the suite of st... Read more

Last Updated on January 20, 2020 Resampling methods are designed to change the composition of a training dataset for an imbalanced classification task. Most of the attention of resampling me... Read more

Last Updated on January 17, 2020 Imbalanced Classification Crash Course.Get on top of imbalanced classification in 7 days. Classification predictive modeling is the task of assigning a label... Read more

Imbalanced classification involves developing predictive models on classification datasets that have a severe class imbalance. The challenge of working with imbalanced datasets is that most... Read more

Imbalanced datasets are those where there is a severe skew in the class distribution, such as 1:100 or 1:1000 examples in the minority class to the majority class. This bias in the training... Read more

Last Updated on January 14, 2020 A common mistake made by beginners is to apply machine learning algorithms to a problem without establishing a performance baseline. A performance baseline p... Read more

Last Updated on January 13, 2020 Model evaluation involves using the available dataset to fit a model and estimate its performance when making predictions on unseen examples. It is a challen... Read more

Classification predictive modeling involves predicting a class label for examples, although some problems require the prediction of a probability of class membership. For these problems, the... Read more

A classifier is only as good as the metric used to evaluate it. If you choose the wrong metric to evaluate your models, you are likely to choose a poor model, or in the worst case, be misled... Read more