Learner, Practitioner & Teacher
Introduction In the rapidly evolving landscape of artificial intelligence, choosing the right approach for a given task is crucial. Two prominent methodologies, Traditional Machine Learning (ML) and Generative Artificial Intelligence (Generative AI), each have their strengths and weaknesses. In this
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Introduction: In the realm of machine learning and statistics, two fundamental analytical approaches—discriminative and generative—play pivotal roles in shaping models and decision-making processes. In this blog post, we will delve into the characteristics of discriminative and generative approaches, highlighting their
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Introduction: Data Exploratory Analysis (EDA) is a crucial step in the data science pipeline that involves understanding and visualizing the characteristics of a dataset before diving into model building. Several Python packages have been developed to streamline and enhance the
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Introduction In the ever-evolving realm of artificial intelligence, two predominant paradigms have emerged: foundational models and traditional machine learning. These approaches exhibit distinct characteristics and applications, making it crucial to understand their differences. In this blog post, we’ll conduct a
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Introduction: In the grand journey of humanity, our past generations foresaw a future centered around three pillars: Products, Knowledge, and Communication. Let’s delve into the evolution of these elements, shaping our lives from the simplest tools to the cutting-edge advancements
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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
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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
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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
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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
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As healthcare providers strive to provide the best possible care to their patients, understanding the effects of different treatments becomes increasingly important. This is especially true for data in longitudinal nature, where patients are followed over time and treatment effects
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