A Comprehensive Guide to Understand and Implement Text Classification in Python
We have seen with parametric mixture models that we can assign group labels to observations using a model, but so far we have assumed that we know how many groups there are a priori. What if we don't know how many groups produced the data? We often want the choice of K to be data-driven.
There are a number of approaches for allocating samples to groups where the number of groups is not pre-determined. We will look at two generative methods here.
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.
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