Logistic Regression Algorithm

Logistic regression is a popular method to predict a categorical response. A special case of generalized linear models predicts the probability of the outcomes. It can be used for both binary and multi-class classification problems.

Logistic Regression Analytics processor is used to analyze data using ML’s Logistic Regression Model.

To us e a Logistic 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 that will be treated as Label column while training a model.

Probability Column: Column name that holds the value of probabilities of predicted output.

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.

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

Thresholds: Specify the threshold parameter for class range.

Number of thresholds should be equal to Number of Output Classes.

ElasticNet Param: Specify the value for ElasticNet Parameter for model training

Reg Param: Specify the value for Regularization Parameter for model training

Max Iterations: Number of Iterations for building ensemble of trees. Number of Output trees is equal to the max iterations specified. This acts as one of the stopping criteria for model training.

Fit Intercept: Whether to fit an intercept term or not.

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.