Machine Learning for Humans, Part 2.3: Supervised Learning III

Things are about to get a little… wiggly. In contrast to the methods we've covered so far -- linear regression, logistic regression, and SVMs where the form of the model was pre-defined -- non-parametric learners do not have a model structure specified a priori. We don't speculate about the form of the function f that we're trying to learn before training the model, as we did previously with linear regression. Instead, the model structure is purely determined from the data. These models are more flexible to the shape of the training data, but this sometimes comes at the cost of interpretability.