Loading subject…
Collaborative filtering
A recommendation method that treats each user as a point defined by their ratings, finds the users nearest to them, and suggests items those neighbours rated highly.
Example
A streaming service suggests a film you have not watched because the three users whose ratings most resemble yours all rated it highly.
Decision tree
A supervised learning model that classifies an example by testing one feature at a time and following the branch that matches, until it reaches a leaf carrying a label.
Example
A triage tree that tests whether temperature exceeds 38 degrees, then whether a cough is present, and ends at a leaf labelled urgent or routine.
Hyperparameters
Parameters set before training that cannot be learned from data.
K-Nearest Neighbours (K-NN)
A supervised learning algorithm that classifies a new example by finding the K training examples closest to it and taking a majority vote of their labels.
Example
With K = 3, a new fruit whose three nearest neighbours are labelled Apple, Apple and Banana is classified as Apple.
Normalization (data preprocessing)
Rescaling a numeric feature into a fixed range, usually 0 to 1, so that features measured in different units contribute to a model on a comparable scale. This is a different process from normalization in relational database design.
Overfitting
A model that fits the data it was trained on so closely that it captures accidental patterns in that data and performs poorly on records it has not seen.
Example
A price model given 214 columns may split the training sales on wall colour, which says nothing about the price of the next house.