One of the most simply classification algorithm one can think of: just see whatever kind of point your new point seems to be closer to, and say it is also of that type! Then it is just a question of defining "close".
Scikit-learn implementation scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html at python/sklearn/knn.py
This is the first thing you have to know about supervised learning:Both of those already have hardware acceleration available as of the 2010s.
- training is when you learn model parameters from input. This literally means learning the best value we can for a bunch of number input numbers of the model. This can easily be on the hundreds of thousands.
- inference is when we take a trained model (i.e. with the parameters determined), and apply it to new inputs
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Supervised learning fits a prediction rule from training data containing both inputs and associated target responses. Regression analysis predicts numerical responses; classification in statistical learning predicts class labels. A loss function specifies which prediction errors matter, and generalization concerns performance beyond the observations used to fit the rule.