Probabilistic models are an important part of machine learning because they give a rigorous and adaptable framework for dealing with uncertainty and generating predictions. Probabilistic models have a ...

We use probabilistic machine learning to look at the calibration problem in a probabilistic framework based on Gaussian processes. This immediately gives a way of encoding prior beliefs about the ...

Analyzing classification model performance is a crucial task for machine learning practitioners. While practitioners often use count-based metrics derived from confusion matrices, like accuracy, many ...

A modelling API which simplifies the creation of probabilistic models. An inference engine, which combines the model with your observations, and handles the complex inference process. The goal of ...

A Machine Learning-Based Method for Assisted Analysis and Decision ... and realizes the classification of hybrid defects by designing a separate classification model of minimum defects and using the ...