Nearest-neighbor density estimation - Nearest-neighbor density estimation estimates how crowded a region of data is from the distances to nearby points: where neighbors are close, the density is...
Nearest-neighbor filter - A nearest-neighbor filter is a data-cleaning rule that removes or corrects points whose nearest neighbors disagree with them: a labeled example surrounded...
Nearest Shrunken Centroid - Nearest shrunken centroid is a classification method that moves class-average feature values toward the overall average before assigning a new example to a...
Neighborhood Components Analysis - Neighborhood Components Analysis is a metric-learning method that reshapes the feature space so that points of the same class end up close together: it...
Neural modeling fields - Neural modeling fields are a research framework associated with Leonid Perlovsky that links observed signals with adaptive internal models through gradually...
Neural scaling law - A neural scaling law is an empirical relationship describing how a neural model's measured performance changes as resources such as model size, training...
Neuralese - Neuralese is an informal term for machine-learned communication or internal representations that do not take the form of ordinary human language.
Neuroevolution of augmenting topologies - Neuroevolution of augmenting topologies, usually called NEAT, is an evolutionary method that searches for both neural-network connection weights and network...
Neuron - A neuron in machine learning is a small computing unit that takes several numeric inputs, combines them with learned weights, and passes the result through...
Neurorobotics - Neurorobotics studies neural models and robotic bodies together, using interaction with an environment to investigate brain-inspired control and embodied...
Node Classification - Node classification is the task of labeling the nodes of a network: given a graph of connected items and labels for some of them, predict the labels of the...
Node (decision tree) - A node in a decision tree is one decision point or conclusion: internal nodes test a condition on a feature, and leaf nodes carry the final prediction.
Noise - Noise is the part of data that varies without meaning: measurement error, random fluctuation, typos, one-off circumstances, the static that no model can...
NOMINATE (scaling method) - NOMINATE is a family of statistical scaling methods that estimates low-dimensional positions from recorded choices, especially legislative roll-call votes.
Non-binary condition - A non-binary condition is a test with three or more possible outcomes: not yes-or-no, but a choice among several branches, such as routing by region into...
Non-Negative Least Squares - Non-negative least squares (NNLS) fits a linear combination to observed data while requiring every fitted coefficient to be zero or positive.
Normal-gamma distribution - The normal-gamma distribution is a joint probability distribution for an unknown mean and an unknown positive precision.
Normal-inverse-Wishart distribution - The normal-inverse-Wishart distribution is a joint probability distribution for a mean vector and a covariance matrix.
Normal-Wishart distribution - The normal-Wishart distribution is a joint probability distribution for a mean vector and a precision matrix.
Nouvelle AI - Nouvelle AI is a historically influential approach to artificial intelligence that emphasizes situated, embodied interaction and the development of useful...
NSynth - NSynth is a neural audio-synthesis research project that learns representations of musical notes and uses them to generate sound.
Numerical data - Numerical data is data that lives on a number line: quantities with magnitude and order, like revenue, temperature, age or count, where arithmetic means...
Oblique condition - An oblique condition is a decision-tree split on a combination of features at once, a slanted line through the data, rather than the usual single-feature...
On-Demand Features - On-demand features are computed at the moment a prediction is requested, from the freshest available data, rather than being pre-computed in a batch and...
One-hot encoding - One-hot encoding turns a category into a set of yes-no columns: 'city' becomes one column per city, with a 1 in the item's own city and 0 everywhere else...
One-hot vector - A one-hot vector is the row of switches that represents one item's category: all zeros except a single 1 in the position of the category the item belongs to.
One-shot learning - One-shot learning is learning to recognize a category from a single example: show the system one photo of a new product, one recording of a new voice, and...
One-vs-one - One-vs-one is a scheme for multi-class classification that trains one binary classifier for every pair of classes: ten classes mean forty-five pairwise...
One-vs-Rest - One-vs-Rest is a scheme for multi-class classification that trains one binary model per class, each answering a single question: is this item a member of my...
Online-Offline Feature Skew - Online-offline feature skew is the mismatch that appears when a feature is computed one way in the training data and a subtly different way in the live...
Online One-Class SVM - An online one-class SVM is a novelty detector trained mostly on examples of normal behavior and updated as new normal data arrives, flagging whatever does...
Open-source artificial intelligence - Open-source artificial intelligence refers to AI systems released with permissions and materials that enable others to use, inspect, modify and share them.
Optimality criterion - An optimality criterion is the rule used to decide which candidate model or solution counts as best.
Optimizer - An optimizer is the procedure that adjusts a model's parameters during training to reduce its errors, step by step, until the model fits the data as well as...
Ordinal regression - Ordinal regression models an outcome whose categories have a meaningful order but whose gaps need not be equal.
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...
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...
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...
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...
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...
Packed data - Packed data is a storage layout where values are stored back to back in a fixed binary form, with no per-value overhead, so a million numbers occupy exactly...
Pair Confusion Matrix - A Pair Confusion Matrix judges a clustering by looking at pairs of items: for every pair, did the clustering correctly put same-kind items together and...
Parameter - A parameter is one of the internal settings a model learns from data during training: the dial positions, like the weights on inputs, that together encode...
Parameter update - A parameter update is one adjustment to a model's internal settings during training: after seeing a batch of data and measuring its errors, the model nudges...