We are excited to announce the latest release of the AI & Analytics Engine - 1.8.0. This release aims to improve the insights and accessibility of the supervised machine learning predictive models. The PI.EXCHANGE team is determined to help users gain more insights about the impact of different features on the models' predictions by introducing Feature importance and Prediction explanation. Furthermore, as a result of the technical improvements for models, batch prediction is also now available for any model without the need for an active deployment.
A step to sense-check machine learning model performance is to check the factors contributing to the models' predictions. In the Engine, for any successful trained supervised machine learning model (regression and classification), users can generate the feature importance score after a model successfully finishes training. For models that predict a label from a list of labels (multi-class classification), the feature importance scores are different for different labels.
In addition to the overall impact score of each feature, users can now investigate how different values of a feature impact a model’s prediction using the prediction explanation feature. For models that predict a label from a list of labels (multi-class classification), prediction explanations are different for different labels. So, a prediction explanation is generated for each label.
Users can view some prediction explanation examples from the data used to build predictive models.
We have made technical improvements so that users can now use the batch prediction feature to upload a file and generate predictions without deploying a model. The batch prediction feature can be found in the Prediction tab at the model detail page.