This post shows how to load and evaluate the model we built in the previous post.
This is post is a walkthrough of creating a Siamese network with FastAI. I had planned to simply use the tutorial from FastAI, but I had to change so much to be able to load the model and make it all work with the latest versions that I figured I would turn it into a blog post. This is really similar to my other post on Siamese Networks with FastAI, except that in this one I will follow on with a post about how to evaluate the model.
Distributions are super important. In this post, I’ll talk about some common distributions, how to plot them, and what they can be used for.
This post contains instructions for how to work with personal access tokens on GitHub.
This is Part II of two posts demonstrating how to get test metrics from a highly unbalanced dataset. In Part I, I showed how you could theoretically estimate the precision, recall, and f1 score on highly unbalanced data. In this part, I’ll do the same thing with a real dataset. To do so, I’ll use the Adult Income Dataset.
In this post, I’m going to walk through how to solve a problem that you might run into when evaluating models on highly unbalanced datasets. Let’s imagine you’re classifying whether people have a really rare disease or not. You asked 100,000 people at random and only found 10 instances of the disease. How are you going to be able to get enough data to train a machine learning model? Fortunately, you know of a treatment center that treats this specific disease.
I had too many failures for one post, so this post describes even more ways not to evaluate models with FastAI.