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01_​Guided_​Analytics_​for_​ML_​Automation

Guided Automation

Download the entire workflow group from the Example Server. This Guided Analytics application is made of this main workflow, which calls minor workflows within the same folder.

Examples Server location: Examples/50_Applications/36_Guided_Analytics_for_ML_Automation

This workflow generates a fully automated web based application to select, train, test, and optimize a number of machine learning models. The workflow was designed for business analysts to easily create predictive analytics solutions by applying their domain knowledge. Each of the components will generate a web page with which the business analyst can interact.

This workflow is made to be deployed on KNIME WebPortal via KNIME Server.

Guided AutomationThis workflow defines a fully automated web based application to select, train, test, and optimize a number of machine learning models. Theworkflow was designed for business analysts to easily create predictive analytics solutions by applying their domain knowledge. Each of thecomponents outputs a web page with which the business analyst can interact.Each component creates a view for you to open and interact with. Right click a component and "Execute and Open Views". After interaction with View, "Apply & Close" in lower right corner. Upload Data and Process Setup1. Upload your data / define file path.2. Select the target column for the prediction.3. Filter columns to exclude from the model.4. Select models and whether to fine-tune the model parameters. Fine-tune Model Parameters or Use Automatic SettingsIf you selected to fine-tune the model parameters:1. Define the parameter values used for optimization.2. Define feature engineering settingsOtherwise the settings will be automatically set. Execution SettingsDecide which executionenvironment you want touse. ML AutomationModel parameters areautomatically optimized andfeatures engineered. Download ModelsCompare and inspectthe results of the modelsand download thedesired ones. Upload Dataset Select Target Parameter Settings Select Models Feature EngineeringSettings IF Switch End IF Execution Settings Filter Columns Automatic ParameterSettings Automatic FeatureEngineering Settings Download Models Feature QualityCalculation Training andValidation of Models Guided AutomationThis workflow defines a fully automated web based application to select, train, test, and optimize a number of machine learning models. Theworkflow was designed for business analysts to easily create predictive analytics solutions by applying their domain knowledge. Each of thecomponents outputs a web page with which the business analyst can interact.Each component creates a view for you to open and interact with. Right click a component and "Execute and Open Views". After interaction with View, "Apply & Close" in lower right corner. Upload Data and Process Setup1. Upload your data / define file path.2. Select the target column for the prediction.3. Filter columns to exclude from the model.4. Select models and whether to fine-tune the model parameters. Fine-tune Model Parameters or Use Automatic SettingsIf you selected to fine-tune the model parameters:1. Define the parameter values used for optimization.2. Define feature engineering settingsOtherwise the settings will be automatically set. Execution SettingsDecide which executionenvironment you want touse. ML AutomationModel parameters areautomatically optimized andfeatures engineered. Download ModelsCompare and inspectthe results of the modelsand download thedesired ones. Upload Dataset Select Target Parameter Settings Select Models Feature EngineeringSettings IF Switch End IF Execution Settings Filter Columns Automatic ParameterSettings Automatic FeatureEngineering Settings Download Models Feature QualityCalculation Training andValidation of Models

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: simon
Created at: 2018-05-15
On NodePit since: 2021-07-08
Last update: 2024-12-21
Created with KNIME version: v4.2.2
Tags: autoMLguided analyticsautomated machine learningguided automationfeature engineeringparameter optimizationhyperparameterhyper-parameteroptimizationfeature selectionWebPortalapplicationweb appmachine learning

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