Learning from data sets containing labels or known outcomes, where the algorithms build models based on the patterns in that “training” data. The resulting models are generalized and can be applied to new, never-before-seen data. Supervised Learning is used for classification and regression problems.
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.
When the machine learning process is completed, the Machine Learning 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.