Supervised automated machine learning (AutoML) algorithms are taught to solve a specific task using Training Data. Training Data contains labels for data columns (features) and known outcomes for the columns (features) to be predicted. Known outcomes may included classifications of two (binary) or more (multi-class) possibilities. Known outcomes that are scalar values (numbers) are used for regression predictions such as forecasts.
After learning from the Training Data, automated machine learning algorithms produce predictions on Production Data. Production Data is a data set with the same columns (features) as the Training Data, except that the columns (features) to be predicted are not included.
When the machine learning process is completed, the AutoML system uses models built from Training Data to add predicted values to the Production Data. Production data with the appended prediction values are output as Predictions data sets.