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General ML configuration related questions

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Where can I find the ML configuration file? Where can I find the ML configuration file?

Where can I find the ML configuration file?

The <ML_HOME>/repository/conf/machine-learner.xml file includes all the ML-specific configurations. 

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How can I change the ML datasource name? How can I change the ML datasource name?

How can I change the ML datasource name?

The default name is jdbc/WSO2ML_DB. You can change it by changing the value of the <DataSourceName> element in the <ML_HOME>/repository/conf/machine-learner.xml file. For more information, see ML-specific configurations. 

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How can I change the sample points size that ML use to generate summary statistics?

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How can I change the sample points size that ML use to generate summary statistics?

How can I change the sample points size that ML use to generate summary statistics?

Default size is 10000. You can change it by changing the value of the <SampleSize> property within the <SummaryStatisticsSettings> element in the <ML_HOME>/repository/conf/machine-learner.xml file. For more information, see ML-specific configurations. 

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How can I change the directory which holds datasets? How can I change the directory which holds datasets?

How can I change the directory which holds datasets?

By default, the <ML_HOME>/datasets/ directory holds datasets, and the default storage type is ‘file’. You can change it by changing the value of the <StorageDirectory> property within the <DatasetStorage> element in the <ML_HOME>/repository/conf/machine-learner.xml file. For more information, see ML-specific configurations. 

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How can I change the directory with models? How can I change the directory with models?

How can I change the directory with models?

By default, the <ML_HOME>/models/ directory holds models, and default storage type is ‘file’. You can change it by changing the value of the <StorageDirectory> property within the <ModelStorage> element in the <ML_HOME>/repository/conf/machine-learner.xml file. For more information, see ML-specific configurations. 

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How can I increase ML thread pool size? How can I increase ML thread pool size?

How can I increase ML thread pool size?

WSO2 ML uses threads in a thread pool to run different tasks such as dataset summary generation and model generation etc. You can control the size of this thread pool by changing the value of the following property in the <ML_HOME>/repository/conf/machine-learner.xml file: <Property name="ml.thread.pool.size" value="100"/> For more information, see ML-specific configurations. 

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Where should I configure the email addresses of recipients whom will be notified upon a model generation?

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Where should I configure the email addresses of recipients whom will be notified upon a model generation?

Where should I configure the email addresses of recipients whom will be notified upon a model generation?

You can configure WSO2 ML to send emails on the completion of a model generation. You can have a comma-separated set of email addresses as the value of the <EmailNotificationEndpoint> property in the <ML_HOME>/repository/conf/machine-learner.xml file. For more information on  configuring email support, see Enabling Email Notifications.

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How can I change

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datasets storage to HDFS?

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How can I change datasets storage to HDFS?

How can I change datasets storage to HDFS?

If you want to change the dataset storage type to HDFS, change the value of the <StorageType> property within the <DatasetStorage> element to ‘hdfs’. For more information, see ML-specific configurations. 

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How can I change model storage to HDFS? How can I change model storage to HDFS?

How can I change model storage to HDFS?

If you want to change the model storage type to HDFS, change the value of the <StorageType> property within the <ModelStorage> element to ‘hdfs’. For more information, see ML-specific configurations. 

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How can I give a HDFS URL?

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How can I give a HDFS URL?

How can I give a HDFS URL?

If you want to store your datasets and models in an HDFS, you need to enter HDFS URL as the value of the <HdfsURL> property in the <ML_HOME>/repository/conf/machine-learner.xml file. For more information, see ML-specific configurations.  

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Data related questions

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What should be the format of my dataset? What should be the format of my dataset?

What should be the format of my dataset?

WSO2 ML currently supports the following formats.

• CSV with comma separated values
• TSV with tab separated values

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Where can I have my data? Where can I have my data?

Where can I have my data?

Data can be retrieved from the following sources.

• File system
• Hadoop distributed file system
• WSO2 Data Analytics Server table

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Do I need to have a header row in my dataset? Do I need to have a header row in my dataset?

Do I need to have a header row in my dataset?

It is not mandatory to have a header row. If the dataset does not have a header row, it will be indicated when you upload the dataset. Then WSO2 ML will generate a header similar to V1, V2 .. Vn. For more information see Exploring Data.

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Is there a file size limit to my dataset? Is there a file size limit to my dataset?

Is there a file size limit to my dataset?

Yes, currently it is 100MB. You can change it via the following property in <ML_HOME>/bin/wso2server.bat file (for Windows) or <ML_HOME>/bin/wso2server.sh file (for Linux).

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Algorithm related questions

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Does ML support numerical prediction?

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Does ML support numerical prediction?

Does ML support numerical prediction?

Yes it does. The following algorithms are available in this version.

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See How to Select an Algorithm in WSO2 ML for more information 

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Does ML support classification algorithms? Does ML support classification algorithms?

Does ML support classification algorithms?

Yes it does. The classification algorithms currently supported are as follows.

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See How to Select an Algorithm in WSO2 ML for more information 

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Does ML support clustering algorithms? Does ML support clustering algorithms?

Does ML support clustering algorithms?

At present, very primitive support is available for clustering. K-means is the only clustering algorithm supported for this ML version. We have plans to improve on this area. Please contact us if you are someone who is interested to learn more about these plans.

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Analysis related questions

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What type of data preprocessing does WSO2 ML support?

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What type of data preprocessing does WSO2 ML support?

What type of data preprocessing does WSO2 ML support?

It supports feature selection and missing value filling/filtering.

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Can I get an insight about the dataset before creating an analysis out of it? Can I get an insight about the dataset before creating an analysis out of it?

Can I get an insight about the dataset before creating an analysis out of it?

Yes, WSO2 ML supports dataset exploration functionality with multiple visualization techniques. See Exploring Data for more information.

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Model related questions

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How can I find the details of a built model? How can I find the details of a built model?

How can I find the details of a built model?

Once you built a model, you can view its model summary, in which you find a summary of the model evaluation. For more information, see Evaluating Models.

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How can I calculate the accuracy for a given model? How can I calculate the accuracy for a given model?

How can I calculate the accuracy for a given model?

For classification type algorithms, generate an accuracy measurement based on the predictions made by the model for the test dataset. Test dataset is extracted from the uploaded dataset and the proportion is configurable for each analyses.

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Can I download a built model?

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Can I download a built model

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You can download a built model or publish it to WSO2 registry. For more information, see Generating Models.

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Do you support exporting models in PMML format? Do you support exporting models in PMML format?

Do you support exporting models in PMML format?

WSo2 ML 1.0.0 does not support PMML format. This is in the roadmap to be provided in future versions.

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Can I use a built model in a Java program? Can I use a built model in a Java program?

Can I use a built model in a Java program?

You can use a built model in a Java program. For a sample on how to use a built model in a Java program, see Using ML Models in a Java Client.

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How can I make predictions for a test dataset using ML UI wizard?

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How can I make predictions for a test dataset using ML UI wizard?

How can I make predictions for a test dataset using ML UI wizard?

Once you build a model, you can make predictions to a test dataset by uploading a csv/tsv file from the Predict page in the ML UI. For more information on making predictions, see Making Predictions Using the ML UI. 

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Can I use a built model in other WSO2 products? Can I use a built model in other WSO2 products?

Can I use a built model in other WSO2 products?

Yes, you can use the generated models in WSO2 ESB with the Predict mediator and in WSO2 CEP with CEP ML Extension.

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the generated models in WSO2 ESB with the Predict mediator and in WSO2 CEP with CEP ML Extension.

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REST API related questions

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What is the root context of the ML REST API? What is the root context of the ML REST API?

What is the root context of the ML REST API?

WSO2 ML REST API root context is ‘/ml’. For more information, see REST API Guide.

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What authentication mechanisms you support? What authentication mechanisms you support?

What authentication mechanisms you support?

WSO2 ML supports basic authentication and cookie based authentication. For more information, see REST API Guide.

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What are the main APIs you expose? What are the main APIs you expose?

What are the main APIs you expose?

WSO2 ML exposes five main APIs. They are configurations, datasets, projects, analyses, and models. For more information, see REST API Guide.