Parameter Range Automated Machine Learning (ASCMO-STATIC)

Model menu > Automated Machine Learning > Parameter Range button

In the Automated Machine Learning window you can specify the settings for the automated machine learning and the range of hyperparameters. In the Parameter Range Automated Machine Learning window there are separate tabs for the MLP and ASC-General options.

The Parameter Range Automated Machine Learning window contains the following elements:

    Modeling Method

    Model Type

    Activate the checkboxes for the models you want to use for automated machine learning.

    Automatic Input Selection

    When enabled, the algorithm automatically selects only the most relevant input features. This helps balance model accuracy and complexity. The selection is based on the globally activated inputs, and each model uses a subset of them.

    This option is ignored if only one global input is selected.

    Random Seed

    Enter a numeric value to initialize the random number generator.

    Setting a fixed seed ensures reproducible training and validation results.

Embedded AI Coder

Use Embedded AI Coder options to make your AutoML process hardware-aware — ensuring models are optimized for real device constraints like processing power, memory, and inference time.

Hardware-aware model training designs models that fit the capabilities and limits of your target device for efficient deployment.

This requires a valid Embedded AI Coder installation.

    Restrict to Embedded AI Coder

    Activate the checkbox to limit automated machine learning settings to those currently supported by Embedded AI Coder. Settings and models not yet compatible will be excluded. See also, Model Export to Embedded AI Coder .

Use Hardware Estimates

When enabled, the system estimates model resource requirements (e.g., RAM, ROM, inference time) based on hardware specifications provided by the selected Target Device.

  • Embedded AI Coder Version:Specifies the version of the Embedded AI Coder used for hardware estimates.

  • Target Device: Select a supported hardware device. The list of available devices depends on the selected Embedded AI Coder Version.

  • RAM (bytes): Defines the maximum available Random Access Memory (RAM) in bytes. Models estimated by Embedded AI Coder to exceed this RAM limit, based on the selected hardware specifications, are automatically skipped.

  • ROM (bytes): Defines the maximum available Read-Only Memory (ROM) in bytes. Models estimated by Embedded AI Coder to exceed this ROM limit, based on the selected hardware specifications, are automatically skipped.

  • Inference Time (µs): Specifies the estimated model inference time per evaluation step, in microseconds. This value serves as an approximate estimate. Models exceeding the defined limit are omitted, but allowing some tolerance is recommended since estimates may vary.

Data

Validation Data

Choose whether the selection of the best model (RMSE) is based on the training or test data, if available.

Output Transformation

Select the transformation type of the output. Using a transformation can improve the model prediction. Not all transformations are available if the training data has negative or zero values.

You can select from the following choices:

  • none: no transformation
  • 1/y: inversion
  • 1/sqrt(y): inverted square root
  • log(y): logarithm
  • sqrt(y): square root

  • Bounded: limited to lower and upper bound

  • log(y+c): logarithm plus constant

 

ClosedModel Setting: Multi Layer Perceptron

    Activate the Multi Layer Perceptron (MLP) checkbox on the General tab if you want to include this element in the machine learning process to find the best result. The more checkboxes you select, the longer it takes.

    Activate the checkboxes of the elements to be used during the machine learning process:

    • Activation Function : Activate the corresponding activation functions for the hidden layers.

    Enter the range of minimum and maximum values for the continuous parameters to be used during the automated machine learning process:

    • Number of Neurons : Enter the range of neurons per layer.

    • Number of Layers: Enter the range of hidden layers.

    • Number of Iterations: Enter the range of iterations.

ClosedModel Setting: ASC Gaussian Process

    Activate the ASC Gaussian Process (ASC GP) checkbox on the General tab if you want to include this element in the machine learning process to find the best result. The more checkboxes you select, the longer it takes.

    Activate the checkboxes of the elements to be used during the machine learning process:

    • Validation Mode: Activate the checkbox if you want Leave-One-Out error on training data to be used to calculate the RMSE. If activated, the Validation Data setting in the General tab is ignored.

    • Kernel: Activate the checkboxes of the corresponding model kernels to be used.

    Enter the range of minimum and maximum values for the continuous parameters to be used during the automated machine learning process:

    • Number of Iterations: Enter the range of iterations.

    • Subset Size: Enter the range of the maximum number of basic functions for the model training.

Default

Sets all parameters to their default values.

OK

Applies your settings and closes the window.

Cancel

Discards your settings and closes the window.

See also

Automated Machine Learning window