7.2 Balancing uncertainty quality and computational considerations

While Bayesian methods have many benefits, there are also trade-offs to consider in terms of memory and computational overheads. These considerations play a critical role in selecting the most appropriate methods to use within real-world applications.

In this section, we’ll examine the trade-offs between different methods in terms of performance and uncertainty quality, and we’ll learn how we can use TensorFlow’s profiling tools to measure the computational costs associated with different models.

7.2.1 Setting up our experiments

To evaluate the performance of different models, we’ll need a few different datasets. One of these is the California Housing dataset, which is conveniently provided by scikit-learn. The others we’ll use are commonly used in papers comparing uncertainty models: the Wine Quality dataset and the Concrete Comdivssive Strength dataset. Let’s take a look...