You will be surprised by how much accuracy you can achieve in just a few kylobytes of resources: Decision Tree, Random Forest and XGBoost (Extreme Gradient Boosting) are now available on your microcontrollers: highly RAM-optmized implementations for super-fast classification on embedded devices.
Let's revamp the post I wrote about word classification using Machine Learning on Arduino, this time using a proper microphone (the MP34DT05 mounted on the Arduino Nano 33 BLE Sense) instead of a chinese, analog one: will the results improve?
Are you looking for a top-performer classifiers with a minimal amount of parameters to tune? Look no further: Gaussian Naive Bayes is what you're looking for. And thanks to EloquentML you can now port it to your microcontroller.
In earlier posts I showed you can run incremental binary classification on your microcontroller with Stochastic Gradient Descent or Passive-Aggressive classifier. Now it is time to upgrade your toolbelt with a new item: One-vs-One multiclass classifier.