In this example, the unlabeled dataset is created using the CSV Generator Snap. This dataset is used as the data input for the Predictor (Classification) Snap. The classification field (Balance class) is to be predicted for this dataset.
The model trained in the Weight Balance Classification – Model Training example is used as the model input. The File Reader Snap is configured to read this model from the SLDB. The JSON Parser Snap is used to parse the output from the File Reader Snap. Below is a preview of the output from the JSON Parser Snap (the model):
To test a model trained on a classification dataset, the Predictor (Classification) Snap must be used.
The Predictor (Classification) Snap requires two inputs:
- An unlabeled dataset for the data input
- The ML model trained on the labeled dataset for the model input
The Predictor Snap is configured as shown below:
Based on its configuration, the output from the Snap includes one class prediction per document, and also includes the confidence level for the prediction. This output is as shown below:
Download this pipeline.