The Giskard hub
SHAP stands for "SHapley Additive exPlanations", and is a unified approach that explains the output of any machine learning model; by delivering cohesive explanations it provides invaluable insight into how predictions are being made and opens up immense possibilities in terms of practical applications. In this tutorial we'll explore how to use SHAP values to explain and improve ML models, delving deeper into specific use cases as we go along.
Thought leadership articles about Machine Learning Operations, Governance, Security, Reliability & Ethics
Machine learning models, despite their potential, often face issues like biases and performance inconsistencies. As these models find real-world applications, ensuring their robustness becomes paramount. This tutorial explores these challenges, using the Ecommerce Text Classification dataset as a case study. Through this, we highlight key measures and tools, such as Giskard, to boost model performance.