Isotonic Regression Algorithm

Isotonic Regression belongs to the family of Regression algorithms. It gives an approximate series of one-dimensional observations with a non-decreasing function. Isotonic Regression Analytics processor analyzes data using ML Isotonic Regression Model.

To use an Isotonic Regression Model in Data Pipeline, drag and drop the model component to the pipeline canvas and right-click on it to configure.

The Configuration Section → of every ML model is identical.

After the Configuration tab comes the Feature Selection → tab. (It is identical for all the models except K Means).

Once Feature Selection is done, perform Pre-Processing → on the data before feeding it to the Model. The configuration settings are identical for all the ML models.

Then configure the Model using Model Configuration.

Model Configuration #

Label Column: Column name which will be treated as label column while training a model.

Feature Column: Column name which will be treated as feature column while training a model.

You can use Vector Assembler/Vector Indexer to generate feature column from multiple input columns.

Prediction Column: Set the columns to be predicted. Value of Prediction Column must be set as “prediction” in order to deploy the model as REST service.

Isotonic: Boolean field to indicate whether the output field should be isotonic/monotonically increasing (True) or antitonic/monotonically decreasing (False).

After Model Configuration, Post-Processing → is done, Model Evaluation → can be performed.

Then, apply the Hyper Parameters → on the model to enable tuning your configuration; after which you can simply add notes and save the Configuration.