Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. Unfortunately, current visualization packages are rudimentary and not immediately helpful to the novice. For example, we couldn’t find a library that visualizes how decision nodes split up the feature space. So, we’ve created a general package (part of the animl library) for scikit-learn decision tree visualization and model interpretation.
Source: How to visualize decision trees
An Observable notebook by Yaroslav Sergienko.
Source: SQL 3d engine (interactive preview) / Observable
Source: Seven reasons to learn Vue.js in 2019 – DEV Community ????
Background Lately I’ve been studying consensus algorithms to bolster my understanding of distributed systems. Consensus algorithms achieve agreement on data that is replicated across many nod…
Source: Backend In the Frontend: Implementing Raft in JS – Matt Ritter
The virtual DOM was a fantastic innovation. It brought about a much more productive way of writing web applications by allowing us to…
Source: The Virtual DOM is slow. Meet the Memoized DOM – freeCodeCamp.org