L 1 loss - L1 loss measures prediction error using the absolute difference between a predicted value and its observed target. For one example it is |prediction − target|.
L 1 regularization - L1 regularization is a penalty added to training that charges the model for the size of its weights, with the distinctive effect of pushing unneeded weights...
L 2 loss - L2 loss commonly means squared prediction error: the square of the difference between a prediction and its observed target.
L 2 regularization - L2 regularization is a penalty added to training that charges the model for the square of its weights, shrinking all weights toward zero to keep any single...
L1-based feature selection - L1-based feature selection uses a model penalized by the sum of absolute coefficient values to identify a smaller set of input features.
L1-norm principal component analysis - L1-norm principal component analysis refers to PCA-related methods that use absolute-value criteria instead of the squared-value criteria associated with...
Label - A label is the known answer attached to an example: the category, number or outcome the model is supposed to learn to predict, such as spam, churned or 42000.
Label bias problem - The label bias problem is a limitation of certain locally normalized sequence models: each state's outgoing transition probabilities must sum to one, which...
Label leakage - Label leakage is the accidental inclusion, in a model's inputs, of information that is derived from or equivalent to the answer, so the model learns to read...
Label noise - Label noise is error or unreliability in the target labels used to train or evaluate a supervised learning system.
Label Propagation - Label Propagation is a semi-supervised method that spreads labels from the few cases that have them through a similarity network, so unlabeled cases inherit...
Label ranking average precision - Label ranking average precision, or LRAP, evaluates how well a model ranks the relevant labels for each example above irrelevant labels.
Label shift - Label shift is a change in how common the target classes are between datasets or operating settings, while the distribution of inputs within each class...
Label space - A label space is the set of all permitted target values for a machine-learning task. Usually written Y, it describes what the system is intended to predict.
Label vector - A label vector is an ordered collection of target values aligned with the examples in a dataset.
Labeled data - Labeled data are examples paired with target information for a defined learning or evaluation task.
Labeled data point - A labeled data point is an example for which a target value has been supplied or determined.
Labeled example - A labeled example is a single training case with its known answer attached: the inputs plus the label, the atomic unit of supervised learning.
Labeled precision - Labeled precision is the proportion of predicted structures that match reference structures in both their structural identity and their label.
Labeled recall - Labeled recall is the proportion of reference structures that a system recovers with the correct structural identity and label.
Labiodental consonants - Labiodental consonants are speech sounds produced with a constriction involving the lower lip and upper front teeth.
Lack of fit - Lack of fit is systematic disagreement between a model's assumed relationship and the pattern supported by the data.
Lack-of-fit sum of squares - The lack-of-fit sum of squares is the part of regression residual variation attributable to discrepancies between observed means at replicated predictor...
Lag operator - The lag operator is a compact notation for shifting a time series backward in time.
Lagged features - Lagged features are inputs built from the past values of a series: yesterday's sales, last week's balance, the same customer a month ago, giving a model...
Lagrange dual function - The Lagrange dual function is obtained by taking the infimum of a problem's Lagrangian over its original decision variables, while holding the multipliers...
Lagrangian - A Lagrangian is a function that combines an optimization objective with weighted constraint expressions. The weights are called Lagrange multipliers.
Lambda - Lambda is the traditional symbol for the regularization strength dial: the number that decides how heavily a model is penalized for large weights, trading...
Lambda return - A lambda return is a weighted combination of multi-step returns used as a learning target in reinforcement learning.
Lamport's distributed mutual exclusion algorithm - Lamport's distributed mutual exclusion algorithm coordinates processes so only one at a time uses a shared resource or enters a protected critical section.
Lanczos - In the supplied image-resampling context, Lanczos refers to a family of interpolation filters built from a sinc function multiplied by a finite-support window.
Land use regression model - A land use regression model is an empirical model that relates measurements at monitoring locations to geographic predictors, then uses those relationships...
Landing zone (software) - A landing zone in cloud software architecture is a prepared environment and supporting foundation into which workloads can be deployed.
Landmark detection - Landmark detection estimates the locations of predefined or otherwise task-relevant reference points in an image or scene.
Landmark point - A landmark point is a consistently identifiable location on an object or shape that can be matched with corresponding locations in other images or examples.
Landmark points - In shortest-path search, landmark points are selected graph vertices whose precomputed distances help estimate the remaining distance between other vertices.
Landmarks - Landmarks are distinctive, locatable points used as reference markers: in vision, named positions on faces or objects; in search and planning, well-known...
Language - A language is a system for expressing and interpreting messages through symbols and conventions.
Language engineering - Language engineering is the practical discipline of designing, building, evaluating, and maintaining systems that process human language.
Language generation - Language generation is the production of language by a computer system to express information, answer a request, or serve a communicative purpose.
Language identification - Language identification is the task of determining which language or languages are present in an input.
Language model - A language model is a model of patterns in language, commonly expressed through probabilities over tokens or token sequences.
Language resource - A language resource is an organized collection of language or speech data, or machine-readable descriptions of language, that people can use in language...
Laplace approximation - A Laplace approximation replaces a probability density near a smooth mode with a Gaussian whose center and curvature match that local peak.
Laplace's equation - Laplace's equation states that the Laplacian of an unknown function is zero: ∇²u=0.
Laplace smoothing - Laplace smoothing, also called add-one smoothing, estimates probabilities from categorical counts by adding one to every category count before normalizing.
Laplacian matrix - A Laplacian matrix is a matrix that represents differences across connections in a graph.
Laplacian smoothing - Laplacian smoothing reduces local variation by updating values or geometric positions according to differences from their neighbors.