Supervised learning in machine learning can be described in terms of function approximation. Given a dataset comprised of inputs and outputs, we assume that there is an unknown underlying function that is consistent in mapping inputs to outputs in the target domain and resulted in the dataset. We then use supervised learning algorithms to approximate […]
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 […]
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 […]
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 […]
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 […]
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 […]
Many binary classification tasks do not have an equal number of examples from each class, e.g. the class distribution is skewed or imbalanced. Nevertheless, accuracy is equally important in both classes. An example is the classification of vowel sounds from European languages as either nasal or oral on speech recognition where there are many more […]
Cancer detection is a popular example of an imbalanced classification problem because there are often significantly more cases of non-cancer than actual cancer. A standard imbalanced classification dataset is the mammography dataset that involves detecting breast cancer from radiological scans, specifically the presence of clusters of microcalcifications that appear bright on a mammogram. This dataset […]
Misclassification errors on the minority class are more important than other types of prediction errors for some imbalanced classification tasks. One example is the problem of classifying bank customers as to whether they should receive a loan or not. Giving a loan to a bad customer marked as a good customer results in a greater […]
Many machine learning models are capable of predicting a probability or probability-like scores for class membership. Probabilities provide a required level of granularity for evaluating and comparing models, especially on imbalanced classification problems where tools like ROC Curves are used to interpret predictions and the ROC AUC metric is used to compare model performance, both […]
