WSO2 Machine Learner provides an interface to configure algorithms to build machine learning models using datasets. These models are used for tasks such as fraud detectionnumerical prediction, anomaly detection classification etcand 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.
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Follow the procedure below to upload the dataset based on which the training model is created.
- 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.
- Click ADD DATASET to open the Create Dataset page.
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 - Click CREATE DATASET to save your changes. The Datasets page opens and the dataset you entered is displayed as follows.
Note that the status of the dataset is Processing. - Click REFRESH. The status of the dataset changes to Processed as shown below.
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- Log into the ML UI if you are not already logged in.
- Click the You have X projects link as shown below.
- Click on the Breast_Cancer_data_analytics_project project to expand it.
- Enter breast_cancer_analysis_1 as the analysis name and click CREATE ANALYSIS. The following page appears displaying the summary statistics.
- 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. Select and clear the checkboxes for categorical features as follows.
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 - Click Next. The Parameters view appears. Enter L2 as the reg type.
- Click Next. The Model view appears. Select Breast_Cancer_Dataset-1.0.0 as the dataset version.
- Click RUN to train the model.
The training model is created as displayed as shown below.
Note that the status is In Progress. - Click REFRESH. The status os the analysis changes as shown below.
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