Data frame analytics enable you to perform different analyses of your data and annotate it with the results. By doing this, it provides additional insights into the data. Outlier detection identifies unusual data points in the dataset. Regression makes predictions on your data after it determines certain relationships among your data points. Classification predicts the class or category of a given data point in a dataset. Inference enables you to use trained machine learning models against incoming data in a continuous fashion.

The process leaves the source index intact, it creates a new index that contains a copy of the source data and the annotated data. You can slice and dice the data extended with the results as you normally do with any other data set. Read How it works for more information.

You can evaluate the data frame analytics performance by using the evaluate data frame analytics API against a marked up data set. It helps you understand error distributions and identifies the points where the data frame analytics model performs well or less trustworthily.

Consult Introduction to supervised learning to learn more about how to make predictions with supervised learning.

Table 1. Data frame analytics overview table

Data frame analytics type Learning type

outlier detection