Base effect - A base effect is the influence of the comparison period's value on a reported rate of change.
Base learner - A base learner is a learning method used as a component of an ensemble.
Base model - A base model is a previously trained model used as the starting point for further adaptation, especially fine-tuning for a particular task or application.
Base rate - A base rate is the underlying frequency or probability of an event in a relevant population before considering a particular test result or signal.
Base relation - A base relation is a relation represented as stored data in a database rather than defined as the result of a query over other relations.
Baseline - Baseline has two important AI-related meanings in the supplied sources. In model evaluation, it is a reference method or result used for comparison.
Basic emotions - Basic emotions are a limited set of emotion categories proposed as fundamental within a particular theory of emotion.
Basis - A basis is a set of linearly independent vectors whose linear combinations produce every vector in a specified vector space.
Basis function regression - Basis function regression is a way of fitting curved relationships by transforming an input into a set of fixed building-block functions, such as powers or...
Batch - In machine learning, a batch is a group of training examples processed together in one step before the model updates its parameters.
Batch active learning - Batch active learning is an approach in which a learning system selects a group of examples for labeling before receiving those labels and updating its model.
Batch gradient descent - Batch gradient descent updates a model's parameters using the gradient of the loss calculated across the entire training dataset at the current parameter...
Batch inference - Batch inference is the use of a trained model to produce predictions for a collection of inputs together, typically outside an immediate user-request path.
Batch Inference Pipeline - A batch inference pipeline is a repeatable program or workflow that takes a collection of input data and a trained model, produces predictions and delivers...
Batch normalization - Batch normalization is a neural-network operation that normalizes selected intermediate values using statistics computed across a training mini-batch, then...
Batch processing - Batch processing runs a defined collection of work or data as a job, usually without requiring a person to interact with each individual item.
Batch size - Batch size is the number of training examples a model works through before it makes one adjustment to itself, a setting that trades speed, memory use and...
BatchBALD - BatchBALD is a Bayesian active-learning acquisition method that selects a batch of examples by estimating how much their labels jointly reveal about a...
Batched automation - Batched automation is automation that processes work in grouped runs at scheduled times rather than handling each item the moment it arrives.
Baum - Welch algorithm - The Baum - Welch algorithm estimates the parameters of a hidden Markov model from observed sequences whose underlying states are unknown.
Bayer pattern - A Bayer pattern is a repeating arrangement of red, green and blue color filters over a single image sensor.
Bayes classifier - A Bayes classifier is a decision rule that minimizes expected classification error when the true distribution of inputs and class labels is known and every...
Bayes error rate - The Bayes error rate is the smallest possible expected misclassification rate for a specified joint distribution of observed features and class labels, when...
Bayes estimator - A Bayes estimator is an estimation rule that minimizes expected loss under a specified probability model and loss function.
Bayes factor - A Bayes factor compares how well two specified models predict observed data.
Bayes linear statistics - Bayes linear statistics is an approach to representing and revising uncertainty using expectations, variances, and covariances rather than requiring a fully...
Bayes-Nash equilibrium - A Bayes-Nash equilibrium, also called a Bayesian Nash equilibrium, is a profile of strategies in a game with incomplete information such that each player's...
Bayes risk - Bayes risk, in the machine-learning sense used here, is the smallest expected loss achievable by a prediction rule given the available information, the...
Bayes theorem - Bayes theorem, usually written Bayes' theorem, is a probability identity relating the probability of a hypothesis after observing evidence to its prior...
Bayesian active learning by disagreement - Bayesian active learning by disagreement, usually abbreviated BALD, is an acquisition criterion that selects an example whose unknown label is expected to...
Bayesian experimental design - Bayesian experimental design selects an experiment by maximizing expected utility under a probabilistic model of unknown quantities and possible observations.
Bayesian filtering - Bayesian filtering is a recursive method for estimating a changing hidden state from noisy observations.
Bayesian game - A Bayesian game is a game-theoretic model in which players have incomplete information about relevant features of the interaction, such as another player's...
Bayesian hidden Markov model - A Bayesian hidden Markov model is a probabilistic sequence model that places prior distributions on uncertain model parameters and updates them using...
Bayesian history matching - Bayesian history matching is an uncertainty-aware approach to identifying simulator input settings that have not been ruled out as inconsistent with...
Bayesian inference - Bayesian inference is the process of updating probability distributions for unknown quantities using observed data and a specified statistical model.
Bayesian information criterion - The Bayesian information criterion, or BIC, is a model-selection score that balances fit to observed data against model complexity.
Bayesian interpretation of kernel regularization - The Bayesian interpretation of kernel regularization explains how certain penalized kernel-learning methods correspond to probabilistic models with priors.
Bayesian lasso - The Bayesian lasso is a Bayesian regression approach that uses Laplace, or double-exponential, priors to shrink coefficients toward zero.
Bayesian learning - Bayesian learning is an approach to learning from data that updates a probability distribution over hypotheses or model parameters using Bayes' theorem.
Bayesian linear regression - Bayesian linear regression is a method for predicting a numerical outcome with a linear model while expressing uncertainty about its coefficients and other...
Bayesian logistic regression - Bayesian logistic regression models a binary outcome using a logistic probability function and prior distributions over regression coefficients.
Bayesian model reduction - Bayesian model reduction is a method for obtaining evidence and posterior quantities for alternative probabilistic models from an already fitted parent...
Bayesian multivariate linear regression - Bayesian multivariate linear regression jointly models several numerical outcomes using linear relationships with predictors.
Bayesian network - A Bayesian network is a probabilistic model represented by a directed acyclic graph and a conditional probability distribution for each variable given its...
Bayesian neural network - A Bayesian neural network is a neural model that represents uncertainty about its parameters using probability distributions and updates that uncertainty in...
Bayesian optimization - Bayesian optimization is a method for choosing successive evaluations of an expensive objective using a probabilistic surrogate model and an acquisition rule.