HeatWave AutoML supports supervised machine learning. That is, it creates a machine learning model by analyzing a labeled dataset to learn patterns that enable it to predict labels based on the features of the dataset. For example, this guide uses the Census Income Data Set in its examples, where features such as age, education, occupation, country, and so on, are used to predict the income of an individual (the label).
Once a model is created, it can be used on unseen data, where the label is unknown, to make predictions. In a business setting, predictive models have a variety of possible applications such as predicting customer churn, approving or rejecting credit applications, predicting customer wait times, and so on.
HeatWave AutoML supports both classification and regression models. A classification model predicts discrete values, such as whether an email is spam or not, whether a loan application should be approved or rejected, or what product a customer might be interested in purchasing. A regression model predicts continuous values, such as customer wait times, expected sales, or home prices, for example. The model type is selected during training, with classification being the default type.