The input is generated by the CSV Generator Snap and is composed of the following fields:
- Relative Compactness
- Surface Area
- Wall Area
- Roof Area
- Overall Height
- Glazing Area
- Glazing Area Distribution
- Heating Load
Use Trainer (Regression) Snap to train the model for the dataset.
This input document is passed through the Type Converter Snap that is configured to automatically detect and convert the data types. In any ML pipeline, you must first analyze the input document using the Profile Snap and the Type Inspector Snap to ensure that there are no null values or that the data types are accurate. This step is skipped in this example for simplicity's sake.
Below is a preview of the output from the Type Converter Snap:
Since the training algorithm was evaluated in the Cross Validator (Regression) Snap, the Trainer (Regression) Snap is configured with the same settings as shown below:
The output from the Snap is the model for the dataset as shown below:
The model is written into a file using the File Writer Snap which is configured as shown below:
Download this pipeline.