Feature Function - A feature function is the reusable piece of logic that computes one feature from raw data: given the inputs, here is exactly how this field gets its value.
Feature hashing - Feature hashing is a trick for turning huge or open-ended sets of values into a fixed number of numeric columns, by passing each value through a hash...
Feature interactions - Feature interactions are the ways two or more input fields affect the outcome jointly, so that the effect of one depends on the value of another.
Feature learning - Feature learning is letting a model discover the useful input representations itself from raw data, instead of a person designing the features by hand.
Feature map - A feature map is the output produced when a learned detector is applied across an input, such as the grid of responses an edge detector leaves at every...
Feature matrix - A feature matrix is the table a model trains on: one row per example, one column per feature, with each cell holding that example's value for that field.
Feature Monitoring - Feature monitoring is watching a live model's input values over time, so you catch the moment the data stops looking like what the model was trained on.
Feature Pipeline - A feature pipeline is the automated path that turns raw data into model-ready features on a schedule: extraction, transformation, validation and delivery...
Feature Selection - Feature selection is choosing which of the available input fields a model should actually use, keeping the ones that carry signal and dropping the rest.
Feature set - A feature set is the chosen collection of input fields a model uses: the full list of what the model may consider, selected, named and versioned as one unit.
Feature shift - Feature shift is the change in an input field's values or distribution between when a model was trained and when it is used, a specific form of drift...
Feature template - A feature template is a reusable pattern for generating a family of similar features: define the pattern once, then stamp out a feature for every value...
Feature vector - A feature vector is one record's values across all the model's input fields, written as an ordered list of numbers: that customer, order or event, as the...
Federated learning - Federated learning is training a shared model across many devices or organizations without moving their raw data to one place: the training goes to the...
Feed-forward neural network density model - A feed-forward neural network density model uses a standard layered network to estimate the probability distribution of the data itself, so it can score how...
Feed-forward neural network regression - Feed-forward neural network regression is using a standard layered network to predict a number, such as a price, a demand or a risk score, from input features.
Feedback - Feedback is information about how things went, returned to the system that produced them, so future behavior can adjust: outcomes flowing back to the actor.
Feedback loop - A feedback loop is the closed circuit in which a system's outputs affect its future inputs: what the system does changes the world it then reads, for better...
Feedback neural network - A feedback neural network includes connections that allow earlier internal activity or later-layer information to influence subsequent processing.
Filters, random fields, and maximum entropy model - The filters, random fields, and maximum entropy model, usually called FRAME, is a probabilistic model developed for texture analysis and synthesis.
Finite mixture model - A finite mixture model describes data as coming from a fixed number of underlying groups, each with its own distribution, so one dataset can be read as...
Fitness approximation - Fitness approximation estimates the quality of candidate solutions using a cheaper substitute for an expensive or unavailable evaluation.
Flow-based clustering - Flow-based clustering groups records by how they move through sequences or networks, clustering journeys rather than snapshots: customers by the paths they...
Flow-based generative model - A flow-based generative model represents a complex probability distribution by transforming a simpler distribution through reversible mappings.
Focused information criterion - The focused information criterion, or FIC, selects a statistical model with attention to a particular quantity the analyst wants to estimate.
Force control - Force control regulates the force or torque a machine applies while interacting with an object or its environment.
Forget gate - A forget gate is the mechanism inside an LSTM network that decides what to erase from its memory: how much of each remembered thing to keep at every step.
Formal concept analysis - Formal concept analysis is a mathematical method for organizing objects and their attributes into a concept hierarchy.
Function transformation - A function transformation is applying a mathematical function to a field's values, such as taking the logarithm of income, so the model meets the data on...
Fuzzy agent - A fuzzy agent is a software agent that uses fuzzy logic in its reasoning or decision rules.
Gabbay's separation theorem - Gabbay's separation theorem is a result in temporal logic concerning the separation of past, present and future reasoning.
Gaussian Process Classification - Gaussian process classification is a method that predicts category probabilities while also saying how uncertain it is, by placing a flexible, smooth prior...
Gene expression programming - Gene expression programming, or GEP, is an evolutionary method that searches for programs or expressions.
Generalization - Generalization is a model's ability to perform well on new cases it has never seen, rather than merely repeating answers about the examples it was trained on.
Generalization curve - A generalization curve is a plot of a model's performance, usually the loss, on training data and on unseen data side by side as training progresses...
Generalized canonical correlation - Generalized canonical correlation analysis examines shared relationships among more than two sets of variables measured on corresponding observations.
Generalized Dirichlet distribution - The generalized Dirichlet distribution is a family of probability distributions for vectors of proportions that sum to one.
Generalized distributive law - The generalized distributive law is a framework for reorganizing calculations by exploiting distributivity and factorization.
Generalized linear classifier - A generalized linear classifier is a classifier that separates classes with a linear boundary computed from the inputs, with the term generalized pointing...
Generalized multidimensional scaling - Generalized multidimensional scaling is a method for comparing surfaces by finding a mapping that preserves distances measured along them as closely as...
Generator - A generator is a model that produces new content, such as text, images or data, rather than labeling or scoring existing content; in the...
Geographical cluster - A geographical cluster is a concentration of events, objects or observations in a particular area.
Gödel machine - A Gödel machine is a theoretical self-improving problem solver that changes its own software only after finding a proof that the change is useful under its...
Gradient boosting - Gradient boosting is a modeling method that builds a strong predictor from many small decision trees added one at a time, each new tree trained to correct...
Grafting (decision trees) - Grafting is a decision-tree learning technique that adds branches to an already constructed tree to improve its classifications.
Grammar systems theory - Grammar systems theory studies collections of formal grammars that cooperate or communicate to generate formal languages.