A deep dive into advanced evaluation for data scientists, discussing why accuracy is often misleading and exploring alternative metrics for classification and regression tasks like ROC-AUC, Log Loss, R², RMSLE, and Quantile Loss.
Additive Decision Trees are a variation of standard decision trees, constructed in a way that can often allow them to be more accurate, more interpretable, or both. This article explains the intuition behind Additive Decision Trees and how they can be constructed.
emlearn is an open-source machine learning inference engine designed for microcontrollers and embedded devices. It supports various machine learning models for classification, regression, unsupervised learning, and feature extraction. The engine is portable, with a single header file include, and uses C99 code and static memory allocation. Users can train models in Python and convert them to C code for inference.
