Correspondence analysis - Correspondence analysis is an exploratory method for studying relationships in a table of nonnegative counts, especially a contingency table.
Cost-sensitive machine learning - Cost-sensitive machine learning accounts for the fact that different prediction errors or actions can have different consequences.
Count sketch - Count sketch is a compact probabilistic data structure for estimating item frequencies in a data stream.
Coupled mixture model - A coupled mixture model is a set of mixture models trained together on related datasets, with links between their components so that evidence from one...
Coupled pattern learner - The Coupled Pattern Learner, or CPL, is a semi-supervised information-extraction method that learns text patterns for categories and relations together.
Crash blossom - A crash blossom is a headline or sentence whose wording invites a wildly wrong reading, named after a real headline about a musician whose career ended in a...
Critic - In reinforcement learning, the critic is the part of the system that judges how good the current situation or action is, while another part, the actor...
Cross decomposition - Cross decomposition is a family of methods that finds hidden dimensions linking two sets of measurements, such as customer survey answers and purchase...
Cross-entropy method - The cross-entropy method is a sampling-based approach to optimization and rare-event estimation.
Cross-fitting - Cross-fitting is a validation technique where predictions for every data point are made by a model that never saw that point in training, giving honest...
Data analysis - Data analysis is the process of inspecting, cleaning and summarizing data to answer a question or support a decision, ranging from simple totals and charts...
Data augmentation - Data augmentation is the practice of creating extra training examples by changing existing ones in ways that keep their meaning, such as flipping or...
Data-driven model - A data-driven model is a model whose relationships or parameters are learned substantially from observed data.
Data exploration - Data exploration is the process of examining a dataset to understand its structure, quality, patterns and limitations before drawing firm conclusions or...
Data leakage - Data leakage is when information from outside a model's legitimate training view, often the answer itself, slips into its inputs, making test results look...
Data type - A data type describes what kind of value a column or field holds, such as whole number, decimal, text, date or true-false, and decides what you can do with...
Dataset shift - Dataset shift is a difference between the data distribution used to develop a model and the distribution encountered during evaluation or use.
Decision boundary - A decision boundary is the dividing line, or surface, in the space of inputs where a classifier switches from predicting one class to another, such as the...
Decision forest - A decision forest is a model made of many decision trees whose answers are combined, usually by voting or averaging, so the group predicts better than any...
Decision tree - A decision tree is a model that predicts an answer through a sequence of yes-or-no style questions about the input, arranged like the branches of a tree...
Decision tree pruning - Decision tree pruning is the practice of cutting branches off a grown decision tree so it stops memorizing the training data's noise and predicts new cases...
Decoder - A decoder is the part of a model that turns an internal numeric representation back into an output people can use, such as words, images or predictions.
Deep Q-Network - A Deep Q-Network is a reinforcement learning system that uses a neural network to estimate the value of each available action in a given situation, so the...
Deep tomographic reconstruction - Deep tomographic reconstruction uses deep-learning methods to recover an internal image or volume from indirect measurements.
Delta feature - A delta feature is an input built from the change between values, such as this month's sales minus last month's, so the model sees movement rather than only...
Denoising - Denoising is removing unwanted noise from data, such as static from audio, grain from images or jitter from measurements, so the underlying signal is easier...
Dense feature - A dense feature is an input whose value is almost always present and meaningful, such as age or order total, as opposed to a sparse feature that is empty or...
Density-based clustering validation - Density-Based Clustering Validation, or DBCV, is a score for assessing clustering solutions using density-based ideas of connection within groups and...
Dependability state model - A dependability state model represents a system through states such as operational, degraded and failed, together with possible transitions between them.
Dependency network (graphical model) - A dependency network is a probabilistic graphical model that describes each variable through a conditional distribution given selected other variables.
Depth - Depth is how many layers or levels a model has, counting from input to output, and more depth lets a model build more complicated ideas from simpler ones.
Description logic - Description logic is a family of formal knowledge-representation languages for describing classes of things, their relationships and individual instances.
Deterministic annealing - Deterministic annealing is an optimization method that starts by treating the problem as soft and blurred, then gradually sharpens it, so the search avoids...
Detrended correspondence analysis - Detrended correspondence analysis, or DCA, is an ordination method developed to summarize ecological community data while modifying certain patterns...
Developmental robotics - Developmental robotics studies how robots can acquire abilities progressively through experience, interaction and learning, drawing inspiration from human...
Differentially private stochastic gradient descent - Differentially private stochastic gradient descent, or DP-SGD, is a training procedure that limits individual training examples' influence by clipping their...
Digital organism - A digital organism is a self-replicating computer program studied in an artificial-life environment where replication, variation and selection can occur.
Dimensionality - Dimensionality is the number of input fields a model must consider at once: one field is one dimension, a spreadsheet with two hundred columns is two...
Discovery system (artificial intelligence) - A discovery system in AI is a system designed to generate or identify candidate scientific concepts, relationships or laws.
Discrete feature - A discrete feature is an input that takes one of a limited set of separate values, such as red, amber or green, rather than any value along a continuous scale.
Discriminator - A discriminator is the judging half of a generative adversarial network, the model that learns to tell real examples from fakes while its partner, the...
Distributional Soft Actor Critic - Distributional Soft Actor Critic, or DSAC, is a family of off-policy reinforcement-learning methods for continuous control that combines maximum-entropy...
Divisive clustering - Divisive clustering is a top-down approach to grouping data: start with everything in one cluster and repeatedly split the least coherent group until each...
Document classification - Document classification assigns one or more category labels to a document based on its content or other relevant information.
Domain adaptation - Domain adaptation adjusts a machine-learning system so that knowledge learned from one data setting can be useful in another.