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

Workflow

Guided 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.
autoMLguided analyticsautomated machine learningguided automationfeature engineeringparameter optimizationhyperparameterhyper-parameteroptimizationfeature selectionWebPortalapplicationweb appmachine learning
Guided AutomationThis workflow defines a fully automated web based application to select, train, test, and optimize a number of machinelearning models. The workflow was designed for business analysts to easily create predictive analytics solutions by applyingtheir domain knowledge. Each of the components outputs a web page with which the business analyst can interact. 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 whichexecutionenvironment you wantto use. ML AutomationModel parameters areautomatically optimizedand featuresengineered. Download ModelsCompare andinspect the results ofthe models anddownload 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 Training andValidation of Models Download Models Feature QualityCalculation Guided AutomationThis workflow defines a fully automated web based application to select, train, test, and optimize a number of machinelearning models. The workflow was designed for business analysts to easily create predictive analytics solutions by applyingtheir domain knowledge. Each of the components outputs a web page with which the business analyst can interact. 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 whichexecutionenvironment you wantto use. ML AutomationModel parameters areautomatically optimizedand featuresengineered. Download ModelsCompare andinspect the results ofthe models anddownload 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 Training andValidation of Models Download Models Feature QualityCalculation

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Resources

Nodes

01_​Guided_​Analytics_​for_​ML_​Automation consists of the following 2182 nodes(s):

Plugins

01_​Guided_​Analytics_​for_​ML_​Automation contains nodes provided by the following 22 plugin(s):