Choosing a Machine Learning Model - KDnuggets

The number of shiny models out there can be overwhelming, which means a lot of times people fall back on a few they trust the most and use them on all new problems. This can lead to sub-optimal results. Today we're going to learn how to quickly and efficiently narrow down the space of available models to find those that are most likely to perform best on your problem type. We'll also see how we can keep track of our models' performances using Weights and Biases and compare them. You can find the accompanying code here.