Last Updated on August 21, 2020 Many binary classification tasks do not have an equal number of examples from each class, e.g. the class distribution is skewed or imbalanced. A popular example is the adult income dataset that involves predicting personal income levels as above or below $50,000 per year based on personal details such as relationship and education level. There are many more cases of incomes less than $50K than above $50K, although the skew is not severe. This […]

Last Updated on March 19, 2020 Classification predictive modeling problems involve predicting a class label for a given set of inputs. It is a challenging problem in general, especially if little is known about the dataset, as there are tens, if not hundreds, of machine learning algorithms to choose from. The problem is made significantly more difficult if the distribution of examples across the classes is imbalanced. This requires the use of specialized methods to either change the dataset or […]

Last Updated on August 21, 2020 Fraud is a major problem for credit card companies, both because of the large volume of transactions that are completed each day and because many fraudulent transactions look a lot like normal transactions. Identifying fraudulent credit card transactions is a common type of imbalanced binary classification where the focus is on the positive class (is fraud) class. As such, metrics like precision and recall can be used to summarize model performance in terms of […]

Last Updated on August 21, 2020 Multiclass classification problems are those where a label must be predicted, but there are more than two labels that may be predicted. These are challenging predictive modeling problems because a sufficiently representative number of examples of each class is required for a model to learn the problem. It is made challenging when the number of examples in each class is imbalanced, or skewed toward one or a few of the classes with very few […]

Last Updated on August 21, 2020 Multiclass classification problems are those where a label must be predicted, but there are more than two labels that may be predicted. These are challenging predictive modeling problems because a sufficiently representative number of examples of each class is required for a model to learn the problem. It is made challenging when the number of examples in each class is imbalanced, or skewed toward one or a few of the classes with very few […]