4.1. Examples of AIT Implementation

In this section, we will show some actual implementations.

Important

By clicking a link leads you to the actual notebook implementation in the GitHub aistairc/qunomon repository.

• eval_model_regression_coefficient_determination

  • This test computes the adjusted coefficient of determination adjusted for degrees of freedom for a model of multiple regression analysis constructed with several explanatory variables and one objective variable.

  • link

• eval_model_image_classify_acc_adversarial_example

  • Generate adversarial sample images from input images and calculate accuracy information (Accuracy, Precision, Recall, F value, AUC) of the input model (image classification model trained with input images).

  • From these accuracy information, the accuracy and stability of the machine learning model can be evaluated.

  • link

• alyz_dataset_table_feature_contribution_shap

  • Internally generate a classification model using XGBboost and calculate SHAP values for the model.

  • SHAP values can be graphed to visualize the contribution of feature variables and to infer which feature variables in the table data are useful and how useful they are for classification.

  • link