Ordered key - value store - An ordered key - value store is a data store that associates keys with values while maintaining a defined ordering of the keys.
Ordered weighted averaging - Ordered weighted averaging, usually abbreviated OWA, combines several numerical values by sorting them and applying weights to their ordered positions.
Ordering constraints - Ordering constraints specify that one action or event must occur before another.
Ordinal interaction - An ordinal interaction is an interaction in which a specified group difference changes in magnitude across levels of another variable without reversing...
Ordinal probit regression - Ordinal probit regression models an ordered categorical outcome using a latent linear score with normally distributed noise.
Ordinal regression - Ordinal regression models an outcome whose categories have a meaningful order but whose gaps need not be equal.
Ordinal scale - An ordinal scale classifies observations into categories with a meaningful order, without establishing equal distances between adjacent categories.
Ordinal tree - An ordinal tree is a rooted tree in which each node's children have a defined order.
Ordinal utility function - An ordinal utility function assigns numbers to alternatives so that their ordering represents a decision-maker's preferences.
Ordinal variable - An ordinal variable is a categorical variable whose categories have a meaningful order.
Ordinary Least Squares - Ordinary Least Squares, usually abbreviated OLS, fits a linear model by choosing coefficients that minimize the sum of squared residuals.
Ordination (statistics) - Ordination is a family of statistical methods that arrange observations in a small number of dimensions to summarize relationships in multivariate data.
Organoid intelligence - Organoid intelligence is a research field exploring biological computation using three-dimensional cultures of brain cells, called brain organoids...
Orientation - Orientation describes the direction in which something is aligned relative to a chosen reference.
Oriented projective geometry - Oriented projective geometry is a form of projective geometry that retains a distinction between opposite homogeneous directions.
Origin function - An origin function, in the BLOG probabilistic modeling language, maps a generated object to an object that participated in its generation.
Original time series - An original time series is a sequence of observations presented without seasonal adjustment.
Orthogonal array - An orthogonal array is a structured table of experimental or test settings with a specified balance property.
Orthogonal array testing - Orthogonal array testing selects software or system test configurations using an orthogonal array: a table in which every combination of levels across any...
Orthogonal contrasts - Orthogonal contrasts are linear comparisons of group means that are uncorrelated under a specified statistical model.
Orthogonal Matching Pursuit - Orthogonal Matching Pursuit is a method for building a sparse model: it selects the few input variables that matter most, one at a time, each time picking...
Orthogonality condition - The orthogonality condition characterizes the nearest-point projection onto a linear subspace: the projected point lies in the subspace, and the remaining...
Orthographic projection - Orthographic projection maps three-dimensional points onto a plane along parallel lines perpendicular to that plane.
Oscillatory neural network - An oscillatory neural network is a computational network whose units have oscillating states and whose interactions are used to process information.
Out-group homogeneity bias - Out-group homogeneity bias is the tendency to perceive members of a group outside one's own as more alike than members of one's own group.
Out-of-bag error - Out-of-bag error is an estimate of predictive error obtained by evaluating each training example using only ensemble members whose bootstrap training...
Out-of-bag evaluation - Out-of-bag evaluation estimates a bagged model's predictive performance by using, for each training example, only the component models that were trained...
Out of control - Out of control describes a process whose observed behavior signals a departure from the stable variation expected under a statistical control model.
Out-of-core - Out-of-core describes computing on data too large to fit in the machine's working memory, by processing it in chunks read from storage, so a dataset that...
Out of vocabulary - Out of vocabulary (OOV) describes an input item that is absent from a system's specified vocabulary.
Outage probability - Outage probability is the probability that a communication link fails to meet a specified operating requirement under a stated model of changing conditions.
Outcome - An outcome is a possible or observed result of a process, action or experiment. In probability, an outcome is one element of a defined sample space.
Outer Join - An outer join combines matching rows from its inputs while also preserving specified unmatched rows.
Outlier - An outlier is an observation that is unusually different from the other observations or from the pattern expected under a specified model.
Outlier detection - Outlier detection is finding the rare items that differ markedly from the rest of the data: the transaction that does not fit, the reading that should not...
Outlier detector - An outlier detector is the specific tool or model that does outlier detection: it takes data items in and returns a score or flag saying how unusual each...
Outlier exposure - Outlier exposure is a training approach that uses an auxiliary dataset of out-of-distribution examples to improve a model's ability to recognize unfamiliar...
Output gate - An output gate is a learned control in a long short-term memory network, or LSTM, that regulates how much of the transformed cell state contributes to the...
Output layer - The output layer is the part of a neural network that produces its externally used prediction or representation.
Outstar - An outstar is a neural learning arrangement in which one source unit sends adaptable connections to several target units.
Overfitting - Overfitting is the failure where a model learns its training data too well, memorizing its noise and accidents along with its real patterns, so it performs...
Overlap - add method - The overlap - add method computes a long signal's convolution by processing shorter input blocks and adding the portions of their outputs that occupy the...
Overlap coefficient - The overlap coefficient measures how much two nonempty sets share relative to the size of the smaller set.
Overlap - save method - The overlap - save method computes a long signal's linear convolution using overlapping input frames and circular convolution.
Oversampling - Oversampling increases the representation of selected examples or classes during training, commonly to address a class-imbalanced dataset.
Owen's T function - Owen's T function is a special mathematical function used to calculate certain normal-distribution probabilities.