Linear Regression Algorithm

Regression is an approach for modeling the relationship between a scalar dependent variable and one or more explanatory variables (or independent variables).

Regression Analytics processor is used to analyze data using ML LinearRegressionModel.

To use a Linear 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.

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.

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.

Num Iterations: Number of iterations of gradient descent to run per update.

ElasticNet Parameter: Sets the ElasticNet mixing parameter for the model.

For alpha = 0, the penalty is an L2 penalty.

For alpha = 1, it is an L1 penalty.

For alpha (0, 1), the penalty is a combination of L1 and L2.

Default is 0.0, which is an L2 penalty.

Reg Param: Regularization parameter for model training.

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.