I get asked all the time about my new sources, so here you go: Data science is too huge to try and capture all the news sources. I try to keep a diverse range to keep my pulse on what’s new and useful. …

We change our conversational style from informal to formal speech. We often do this without thinking when talking to our friends compared to addressing a judge. Computers now have this capability! I use textual style transfer in this post to convert informal text to formal text. …

Data scientists often do not have large amounts of labeled data. This issue is even graver when dealing with problems with tens or hundreds of classes. The reality is very few text classification problems get to the point where adding more labeled data isn’t improving performance. SetFit offers a few-shot…

Data scientists often overstate the certainty of their predictions. I have had engineers laugh at my point predictions and point out several types of errors in my model that create uncertainty. Prediction intervals are an excellent counterbalance for communicating the uncertainty of predictions. Conformal inference offers a model agnostic technique…

This post covers 3 easy-to-use 📦 packages to get started. You can also check out the Colab 📓 companion notebook at https://bit.ly/raj_explain and the Youtube 🎥 video for a deeper treatment. Explanations are useful for explaining predictions. In the case of text, they highlight how the text influenced the prediction…

Are you looking for better training data for your models? Let me tell you about dynamic adversarial data collection! I had a large enterprise customer asking me to incorporate this workflow into a Hugging Face private hub demo. Here are some resources I found useful: Chris Emezue put together a blog post: “ How to train your model dynamically using adversarial data” and a real-life example using MNIST using Spaces.

For those running deep learning models, MNIST is ubiquitous. This dataset of handwritten digits serves many purposes from benchmarking numerous algorithms (it's referenced in thousands of papers) and as a visualization, it's even more prevalent than Napoleon’s 1812 March. The digits look like this: There are many reasons for its…

Tree-based methods excel in using feature or variable interactions. As a tree is built, it picks up on the interaction of features. For example, buying ice cream may not be affected by having extra money unless the weather is hot. …

I was recently trying various outlier detection algorithms. For me, the best way to understand an algorithm is to tinker with it. I wanted to share my recent work on a shiny app that allows you to play around with various outlier algorithms. The shiny app is available on my…