Overviewedit

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.

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.

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.

For the available types of data frame analytics and the evaluation methods, consult the table below.

Table 1. Data frame analytics overview table

Data frame analytics typeLearning typeEvaluation type

outlier detection

unsupervised

binary soft classification

regression

supervised

regression

classification

supervised

classification