Median - The median is a value that divides an ordered distribution so that at least half the observations or probability lie at or below it and at least half lie at...
Median absolute error - Median absolute error is a prediction-error measure obtained by taking the median of the absolute differences between actual values and their corresponding...
Median test - The median test, commonly called Mood's median test, is a nonparametric procedure for comparing the population medians of two or more independent groups.
Mediation (statistics) - Mediation in statistics concerns an intermediate variable through which an exposure or intervention may affect an outcome.
Mendelian randomization - Mendelian randomization, or MR, uses genetic variants as instrumental variables to investigate whether an exposure has a causal effect on an outcome.
Mesokurtic - Mesokurtic describes a distribution whose kurtosis equals the normal distribution's benchmark: Pearson kurtosis of three, or excess kurtosis of zero.
Meta-analysis - Meta-analysis is the statistical synthesis of results from multiple studies addressing a sufficiently related question.
Meta-regression - Meta-regression is a statistical method that relates study-level effect estimates to characteristics of the studies contributing to a meta-analysis.
Metamodel calibration - Metamodel calibration, in the computer-experiment sense used here, estimates unknown parameters of a model by learning an inverse relationship from...
Method error - Method error is the component of measurement error attributable to how a measurement is obtained.
Metrology study - A metrology study evaluates the capabilities and limitations of a measurement system for a defined use.
Mini Batch K-Means - Mini Batch K-Means is a variant of k-means clustering that updates cluster centers using small subsets of observations rather than repeatedly processing the...
Minimization (clinical trials) - Minimization in clinical trials is a sequential allocation method that seeks to keep treatment groups balanced on selected participant characteristics.
Minimum chi-square estimation - Minimum chi-square estimation chooses model parameters to minimize a chi-square discrepancy between observed data and model predictions.
Misuse of p-values - Misuse of p-values means calculating, selecting, or interpreting p-values in ways that support conclusions the statistical procedure does not justify.
Mixed-data sampling - Mixed-data sampling, usually abbreviated MIDAS, is a regression approach for relating time series observed at different frequencies.
Mixed model designs - Mixed model designs commonly refer to studies combining at least one between-subjects factor with at least one within-subjects, or repeated-measures, factor.
Mixed sampling - Mixed sampling is a sampling plan that combines two or more basic selection methods.
Mixture distribution - A mixture distribution is a probability distribution formed by combining component distributions with specified probabilities, called mixing weights.
Mode - The mode is a value or category with the greatest frequency in a dataset, or greatest probability in a discrete distribution.
Model - A model is a structured representation used to describe relationships, generate predictions, simulate behavior, or support reasoning about a system.
Model assertion - A model assertion is a check over the inputs and outputs of one or more machine-learning models that flags behavior inconsistent with an expected property.
Model based imputation - Model based imputation replaces missing values using an estimated or specified relationship in the data.
Model capacity - Model capacity is the range or complexity of relationships that a model class can represent and potentially learn.
Model card - A model card is structured documentation that accompanies a machine-learning model and explains its intended uses, evaluation evidence, relevant...
Model cascading - Model cascading is an inference-system strategy that directs requests among models with different capabilities and resource costs, often allowing a smaller...
Model-Centric ML - Model-centric machine learning is an approach that emphasizes improving a model's architecture, learning algorithm or hyperparameters while working with a...
Model checking - Model checking is a method of determining whether a formally specified property holds in the interpretations or system behaviors represented by a model.
Model debugging - Model debugging is the systematic investigation and correction of undesirable behavior in a machine-learning model and the data, code and processes that...
Model decay - Model decay is a deterioration in a deployed machine-learning model's useful performance over time.
Model-Dependent Transformations - Model-dependent transformations are feature-processing steps configured for a particular model and applied consistently when that model is trained and used...
Model editing - Model editing is the targeted modification of a trained model's behavior or represented information without repeating its complete original training process.
Model extraction - Model extraction is the recovery or approximation of a machine-learning model's functionality, structure or parameters through access to the model or its...
Model-Independent Transformations - Model-independent transformations are data-processing steps that produce features whose definitions are not tied to one particular downstream model.
Model Inference - Model inference is the use of a trained machine-learning model to produce outputs from input data.
Model inventory - A model inventory is an organized, maintained record of the models an organization develops, obtains or uses, together with information needed to understand...
Model inversion - Model inversion is a privacy-related inference process that uses access to a trained model, its outputs or related information to infer sensitive attributes...
Model overlay - A model overlay is a documented adjustment applied to a model's inputs, parameters or outputs to address a limitation, missing risk or circumstance that the...
Model parameter - A model parameter is a quantity that helps specify a particular model within a family of possible models.
Model preference - Model preference is a way of ranking or partially ordering the interpretations that satisfy a logical knowledge base so that conclusions can be drawn from...
Model router - A model router is a component that chooses which available model should handle a particular input or task.
Model selection - Model selection is the process of choosing a model or modeling configuration from a set of candidates using evidence and criteria suited to a task.
Model training - Model training is the process of fitting a machine-learning model to data or experience according to a specified learning objective.
Moderated mediation - Moderated mediation describes a model in which an indirect relationship between a predictor and an outcome through a mediator varies with the value of...
Moderation (statistics) - Moderation in statistics occurs when the relationship between a predictor and an outcome depends on another variable, called a moderator.
Modified Kumaraswamy distribution - The modified Kumaraswamy distribution is a continuous probability distribution for values strictly between zero and one.