SHAPtyökalujen

SHAPtyökalujen, which translates to SHAP tools in English, refers to a collection of Python libraries designed to explain the predictions of machine learning models. The core concept behind these tools is the SHAP (SHapley Additive exPlanations) value, a unified measure of feature importance derived from cooperative game theory. SHAP values attribute the contribution of each feature to a specific prediction, allowing users to understand why a model made a particular output.

The SHAPtyökalujen ecosystem provides implementations for calculating SHAP values efficiently across various model types, including tree-based models, deep learning networks, and general linear models. Key functionalities include generating local explanations for individual predictions, as well as global explanations that summarize feature importance across an entire dataset. These explanations can be visualized in intuitive ways, such as force plots, summary plots, and dependence plots, making complex model behaviors more accessible to both data scientists and domain experts. The goal of SHAPtyökalujen is to foster trust and transparency in machine learning by offering robust and theoretically sound methods for model interpretability.