Shuyang

Exploring PyMC’s Insights with SHAP Framework via an Engaging Toy ExampleSHAP values (SHapley Additive exPlanations) are a game-theory-based method used to increase the transparency and interpretability of machine learning models. However, this method, along with other machine learning explainability frameworks, has rarely been applied to Bayesian models, which provide a posterior distribution capturing uncertainty in parameter estimates instead of point estimates used by classical machine learning models.While Bayesian…

A use case of indoor temperature modelization to choose the optimal heating powerWhile machine learning has shown tremendous success in many scientific domains, it remains a grand challenge to follow basic physics laws. Multiple examples have shown that merely data-driven solutions fail to follow the physics laws. At the same time, some developed even mature theories in science would help guide AI to converge to a better solution that makes sense physically.At the same time, the majority of physical models do not provide…

Inference for Runge-Kutta integration with Pymc3Image by Brian AsareMachine learning methods have played an essential role in epidemiology research. Data scientists interested in this domain are lucky since they never have to search for a data-driven solution from scratch. On the contrary, they will be guided by some well-studied mathematical models.This post explains how to use Pymc3, a Python package for Bayesian statistical modeling, to build a bayesian inference to predict the disease spread informed by the most basic…

An introduction and a case studyImage by Evan DennisAs explained in my previous post, the framework of SHAP values, widely used for machine learning explainability has unfortunately failed to reflect the casual structure in its results. Researchers have been proposing possible solutions to remove such a limitation. In the article, I will be reviewing one of the proposed alternatives, the Causal SHAP values (CSVs), and give a simple example with detailed computation to illustrate the difference between CSVs and the…

Two examples of the weak points of SHAP values and an overview of possible solutionsSHAP values seem to remove the trade-off between the complexity of machine learning models and the difficulty of interpretation, encouraging researchers and data scientists to design algorithms without worrying about how to understand the prediction given by any black box. But is it always case that SHAP can explain everything property?In this post, we would like to discuss one important weak point of SHAP values by illustrating some…

How they differ from each other and how to deal with them with TensorFlow ProbabilitySource: PexelsMachine learning, including deep learning, is inseparably connected to uncertainty occurring in various facets. In this post, we will discuss the difference between aleatoric and epistemic uncertainty, the two categories of uncertainty, especially in the setting of deep learning. We will also give an example of TensorFlow Probability to visualize the two types of uncertainty. Please check the notebook for more details of the…

An example based on experience of my own familyIn the context of climate change and the energy crisis, energy efficiency has been a topic in focus. Buildings, responsible for the majority of energy consumption, are worth considering a smart strategy to improve the usage of energy. Some people will argue that things on a global scale are too far away from me, but even in the context of today’s increasingly expensive energy, even from the perspective of saving money, this issue deserves more discussion.In this context, I…