Category: Explainable AI

Introduction: In the ever-evolving landscape of marketing, the ability to communicate precisely with the right client at the right moment is a strategic imperative for any company. The key to success lies in personalized strategies that encourage conversion, engagement, and

There are several types of feature importance methods that can be used to explain how a machine learning model is making its predictions. Here are some of the most common types: Permutation Importance: This method measures the importance of each

Partial dependence plots (PDP) are a type of model-agnostic XAI technique that allows us to visualize the relationship between an input feature and the model’s output. PDPs show how the predicted outcome changes as we vary the values of a

SHAP (SHapley Additive explanations) is another popular method for explaining the predictions of machine learning models. In this blog post, we will explore the SHAP technique, how it works, and its application in healthcare using a use case.   What

LIME (Local Interpretable Model-agnostic Explanations) is a popular method for explaining the predictions of machine learning models, especially black-box models. In this blog post, we will explore the LIME technique, how it works, and its application in healthcare using a

There are various methods to compute the global importance of features in machine learning models. Here are some of the most common methods: Permutation Importance: This method measures the impact of shuffling a feature on the model’s performance. It involves

Explainable AI (XAI) is becoming increasingly important in today’s world of machine learning and artificial intelligence. As these technologies are increasingly used to make critical decisions, it is essential that they are transparent and understandable to ensure their decisions are

Explainable AI (XAI) refers to a set of techniques and approaches used to help humans understand how machine learning and artificial intelligence systems make decisions. There are two main types of XAI methods: model-agnostic and model-specific. In this blog post,

NEED Explainable AI (XAI) refers to the development of AI systems that can provide explanations for their decision-making processes. The importance of XAI is increasing as AI systems become more prevalent in various fields. In order to achieve XAI, several

NEED Explainability is a key factor in ensuring that healthcare applications are safe, reliable, and trustworthy. In an industry where decisions can have life-altering consequences for patients, it is essential that algorithms and other technologies used in healthcare are transparent