Heating load prediction – Cross validation

This pipeline demonstrates a typical cross validation exercise for a dataset before a model is trained to prediction the target field. The dataset is a record of various aspects of a building. The building's required heating load depends upon each of these aspects. The cross validation is to validate the model's ability to predict this heating load.


Cross Validator (Regression) Example pipeline

Download this pipeline.
  1. Configure the CSV Generator Snap to pass the input data.
    Note: In this example, we use the CSV Generator Snap to create a dataset containing the following fields:
    • Relative Compactness
    • Surface Area
    • Wall Area
    • Roof Area
    • Overall Height
    • Orientation
    • Glazing Area
    • Glazing Area Distribution
    • Heating Load
  2. Configure the Cross Validator (Regression) Snap to perform K-fold Cross Validation.

    The Cross Validator (Regression) Snap splits the dataset into training and test sets, which are used to evaluate the selected ML algorithm.

    Cross Validator (Regression) Snap Configuration Cross Validator (Regression) Snap Output

    Cross Validator (Regression) Snap Configuration
    Cross Validator (Regression) Snap Output
    Note: You can optionally write the output to a file using the downstream File Writer Snap for storage or further analysis.
To successfully reuse pipelines:
  1. Download and import the pipeline in to the SnapLogic Platform.
  2. Configure Snap accounts, as applicable.
  3. Provide pipeline parameters, as applicable.