A recent study utilized a multiclass classification model with a dataset comprising 100 samples and 5 features. The data was split into 80% for training and 20% for testing. The model employed was an XGBoost classifier, which is commonly used for its efficiency in handling various types of data. The labels for the classification were randomly assigned into three categories: ‘class_0’, ‘class_1’, and ‘class_2’. This setup was designed to test the functionality of SHAP (SHapley Additive exPlanations) values, which help in interpreting the output of machine learning models. However, the study encountered issues with the SHAP plot, suggesting potential problems with the input data rather than the code itself. This highlights the importance of data quality in machine learning applications.
Source: stackoverflow.com









