Introduction 

Spark plugins that trains and classify data based on Multinomial/Binary Logistic Regression.

Use-case

User Stories

1. User should be able to train the data.
2. User should be able to classify the test data using the model build while training the data.
3. User should be able to provide the list of columns(features) to use for training.
4. User should be able to provide the list of columns(features) to classify.
5. User should be able to provide the column to be used as prediction field while training/classification.
6. User should be able to provide the number of features to be used while training/classification.
7. User should be able to provide the number of classes to be used while training/classification.
8. User should be able to provide the file set name to save the training model.
9. User should be able to provide the path of the file set.

User Stories

Example

Suppose the Trainer plugin gets below records to train the Logistic Regression Model:

Trained on the above records, trainer plugin will provide the create regression model and save it to a Fileset location provided by the  user.

Implementation Tips

Design 

Logistic Regression Trainer

Input Json Format

{
  "name": "LogisticRegressionTrainer",
  "type": "sparksink",
  "properties": {
        "fileSetName": "logical-regression-model",
        "path": "/home/cdap/model",
        "fieldsToClassify": "Starter,Dessert",
        "predictionField": "Tip",
        "numFeatures": "2",
        "numClasses": "2"
   }
}

 Plugin will take above inputs from user and trains the model based on "fieldsToClassify" and  "predictionField" fields as features and label points respectively.

 "fieldsToClassify" can include multiple columns of structure record as features.

 "predictionField" should be the column from input structure record containing the data to be treated as label for prediction.

The model generated from this plugin will further be used by Logistic Regression Classifier plugin to classify the input data.

•  User stories documented 
•  User stories reviewed 
•  Design documented 
•  Design reviewed 
•  Feature merged 
•  Examples and guides 
•  Integration tests 
•  Documentation for feature 
•  Short video demonstrating the feature