A Github Gist containing a Python script for text classification using the TxTail API
This article discusses the limitations of Large Language Models (LLMs) in classification tasks, focusing on their lack of uncertainty and the need for more accurate performance metrics. New benchmarks and a metric named OMNIACCURACY have been introduced to assess LLMs' capabilities in both scenarios with and without correct labels.
This article discusses the importance of understanding and memorizing classification metrics in machine learning. The author shares their own experience and strategies for memorizing metrics such as accuracy, precision, recall, F1 score, and ROC AUC.
use of the LLM to perform next-token prediction, and then convert the predicted next token into a classification label.
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.
A ready-to-run tutorial in Python and scikit-learn to evaluate a classification model compared to a baseline model
LLMS popularized zero-shot learning, or "prompt engineering" which is drastically easier to use and more effective than labeling data. You can also retrofit "prompt engineering" onto good old fashion ML like text classifiers.
