Quick Start Guide

WSO2 Machine Learner provides an interface to configure algorithms to build machine learning models using datasets. These models are used for tasks such as numerical prediction, classification and clustering.

This guide walks you through the basic features of WSO2 ML to get you started. For this purpose, a dataset is analyzed and a ML model is trained to predict the possibility of a person suffering with breast cancer when data relating to a set of other bodily characteristics is provided.

The dataset used in this scenario contains 10 features to provide data on bodily characteristics, and a response variable with the following labels.

The following steps demonstrate how the Machine Learner can use this dataset to make a prediction.

Step 1: Create a dataset

Follow the procedure below to upload the dataset based on which the training model is created.

1. Log into the ML UI (default URL: https://127.0.0.1:9443/ml) using admin as both the username and password. The following is displayed in the Home page.
    
2. Click ADD DATASET to open the Create Dataset page. 

3. In the Data Source field, click Choose File and browse for the <ML_HOME>/samples/tuned/naive-bayes/breastCancerWisconsin.csv file. Enter values for the rest of the parameters as shown below.
   

   Parameter Name	Value
   Dataset Name	Breast_Cancer_Dataset
   Version	1.0.0
   Description	Breast cancer data in Wisconsin.
   Source Type	File
   Data Format	CSV
   Column Header Available	Yes

4. Click CREATE DATASET to save your changes. The Datasets page opens and the dataset you entered is displayed as follows.
    

Step 2: Create a project

Follow the procedure below to create a project for the dataset uploaded.

1. Log into the ML Management Console if you are not already logged in.
2. Click ADD PROJECT. 
   
   
   If you are already logged in, you can click CREATE PROJECT in the DATASETS page as shown below. 
   

3. In the Create Project page, enter information as shown below.
   

   Parameter Name	Description
   Project Name	Breast_Cancer_data_analytics_project
   Description	This project performs predictive analysis on the breast cancer data in Wisconsin.
   Dataset	Breast_Cancer_Dataset

4. Click Create Project to save the information. The project is displayed in the Projects page as follows.
    

Step 3: Create an analysis and train a model

Follow the procedure below to analyse the Breast_Cancer_Dataset dataset, and then create a training model based on that analysis. 

1. Log into the ML UI if you are not already logged in. 
2. Click the You have X projects link as shown below.
   
3. Click on the Breast_Cancer_data_analytics_project project to expand it.
4. Enter breast_cancer_analysis_1 as the analysis name and click CREATE ANALYSIS. The following page appears displaying the summary statistics.
    
5. Click Next without making any changes to the summary statistics.
   
   The Explore view opens. Note that Parallel Sets and Trellis Chart visualisations are enabled, and Scatter Plot and Cluster Diagram visualisations are disabled. This is determined by the feature types of the dataset. 

6. Click Next. The Algorithms view is displayed. Enter values as shown below.
   

   Parameter	Value
   Algorithm name	LOGISTIC REGRESSION L_BFGS
   Response variable	Class
   Train data fraction	0.7

7. Click Next. The Parameters view appears. Enter L2 as the reg type.
    
8. Click Next. The Model view appears. Select Breast_Cancer_Dataset-1.0.0 as the dataset version.
    
9. Click RUN to train the model.
   The training model is created as displayed as shown below.
    

Step 4: Predict using the model

Follow the procedure below to make a prediction based on the training model you created.

1. Log into the ML UI if you are not already logged in. 
2. Click the You have X projects link as shown below to open the Projects window.
    
3. Click MODELS for the breast_cancer_analysis_1  analysis.
   
4. Click Predict on the model displayed.
    

5. Enter values in the Predict page as shown below.
   

   Parameter Name	Value
   Prediction Source	Feature values
   SampleCodeNumber	1018561
   ClumpThickness	2
   UniformityOfCellSize	1
   UniformityOfCellShape	1
   MarginalAdhesion	1
   SingleEpithelialCellSize	2
   BareNuclei	1
   BlandChromati	1
   NormalNucleoli	1
   Mitoses	5

6. Click Predict. The prediction is displayed as follows.