1.1.12 Exploring Model Details

Feature Visualization

The Model Visualization tab provides feature visualization as a 2-D comparison plot and a density plot.

Confusion Matrix

The Confusion Matrix tab shows the averaged confusion matrix for the validation data. The confusion matrix shows how well the model performed at recognizing each class. It also provides information about how the model misclassifies classes. The confusion matrix for models generated by the AutoML pipeline is created by averaging across the results of the validation data sets for each fold.

Feature Summary

The Feature Summary tab shows which feature extractors and sensors were used to feed into the model. The Feature Summary tab contains information about the features that are used during the feature extraction step of the Knowledge Pack. This simple example required only two feature extractors to generate a high accuracy model. The feature Category generator is shown in the first column, which describes the family type a feature generator belongs to. The Generator column has the name of the feature extractor, references the feature generator when building custom pipelines. The Sensors column describes the sensors used as inputs for the feature extractor.

Model Summary

The Model Summary tab describes the classifier, classifier parameters and training algorithm that generates the final model. In general, this will have information about the classifier name, the training algorithm used to train the model, along with any hyperparameters that were set for training. The “uuid” field is the unique identifier for this model.

Pipeline Summary

Model pipelines consist of data input, signal conditioning, signal preprocessing, feature extraction, sampling, and model training. The Pipeline Summary provides the steps graphical representation, which will be part of the Knowledge Pack.

Knowledge Pack Summary

Knowledge Packs consist of data input, signal conditioning, signal preprocessing, feature extraction and classification. The Knowledge Pack Summary provides the graphical representation of the steps that will be part of the Knowledge Pack.