SymetryML6.1
  • Introduction
  • Guides
    • Onboarding Guide
    • Technical Requirements
    • Admin User Guide
    • Installation Guide
      • Installation Guide - GPU
      • Installation Guide - Spark
  • SymetryML GUI
    • ML Toolkit
      • The SymetryML Difference
      • Data Mining Lifecycle
      • SymetryML Concepts
      • Data Sources
      • Streams
      • Encoders
      • Projects
      • Models
    • Sequence Models
    • SymetryML Federated Learning
      • Creating the Federation
      • Load data to local project
      • Requesting Federation Information from Admin Node
      • Joining a Federation with a peer node
      • Federated Data & Modelling
      • Appendix
    • DEM Generator
  • SymetryML Rest Client
    • REST API Reference Guide
      • SymetryML REST API Security
      • SymetryML JSON API Objects
      • Encoder Object REST API
      • SymetryML Projects REST API
      • About Federated Learning
      • Hipaa Compliance and Federated Learning
      • Federated Learning API
        • Federated Learning Topologies
        • Federated Learning with Nats
        • Federated Learning with AWS
        • Fusion Projects
      • Exploration API
      • Modeling API
      • Exporting and Importing Model
      • Third Party Model Rest API
      • SymetryML Job Information
      • Prediction API
      • Data Source API
      • Project Data Source Logs
      • Stream Data Source API
      • AutoML with SymetryML
      • Transform Dataframe
      • Select Model with SymetryML
      • Auto Select with SymetryML
      • Tasks API
      • Miscellaneous API
      • WebSocket API
      • Appendix A JSON Data Structure Schema
      • Appendix B Sample Code
  • SymetryML SaaS
    • SaaS Homepage
    • SaaS Dashboard
    • SaaS Account
    • SaaS Users
    • SaaS Licence
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  1. SymetryML GUI

ML Toolkit

PreviousInstallation Guide - SparkNextThe SymetryML Difference

Last updated 2 years ago

This user guide is intended for analysts using SymetryML in their next data-mining project. While this document does not assume extensive background in data modeling, the reader should have a rudimentary understanding of data-mining methodology, as well as the necessary domain knowledge related to their field of application.

A brief introduction to data-mining concepts will be provided in this guide, along with instructions on applying these concepts to SymetryML workflow. The core intention of this document is to provide readers with a brief summary of the information needed to implement SymetryML successfully into their data-driven projects and to cover typical usage scenarios.

Related Documents

While this guide provides an introduction to data-mining concepts and general guidance on navigating the ML Toolkit, more specific guides dealing with sequence data and Federated Learning can be found here:

Document Conventions

This manual also uses the following typographic conventions.

Conventions

Convention

Description

Bold

Indicates text on a Web page, other than the page title, including menus, menu options, buttons, fields, and labels.

Italic or < > angled brackets

Indicates a variable, which is a placeholder for actual text provided by the user or system. Angled brackets (< >) are also used to indicate variables.

screen/code

Indicates text that is displayed on screen or entered by the user.

[ ] square brackets

Indicates optional values.

{ } braces

Indicates required or expected values.

| vertical bar

Indicates that you have a choice between two or more options or arguments.

Sequence
Federation