Bucketing
Bucketing, also called binning or discretization, is a feature engineering technique in machine learning that converts a continuous feature into a small number of discrete categories by dividing its range of…
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COCO dataset
COCO (Common Objects in Context) is a large-scale dataset for object detection, image segmentation, keypoint detection, and image captioning.
Categorical Data
Categorical data, also called qualitative data, is data whose values are discrete labels or groups (such as colors, country names, or blood types) rather than measurable quantities, so they cannot be…
Class-Imbalanced Dataset
A class-imbalanced dataset is a dataset in which the distribution of examples across the target classes is significantly unequal, so that one class (the majority class) contains far more samples than one or…
Common Crawl
Common Crawl is a nonprofit 501(c)(3) organization that maintains a free, open repository of web crawl data, and it is the single largest publicly available source of text used to train large language models.
Continuous Feature
A continuous feature is a numeric input variable in machine learning and statistics that can take any value within a range, including decimals and fractions, rather than a fixed set of categories or counts.
Convenience Sampling
Convenience sampling (also called grab sampling, accidental sampling, or opportunity sampling) is a non-probability sampling method in which data points or participants are selected because they are easy to…
Coverage Bias
Coverage bias is a type of selection bias that occurs when the method used to collect data systematically excludes part of the target population
Data Augmentation
Data augmentation is a set of techniques that artificially expand the size and diversity of a training dataset by applying label-preserving transformations to existing examples, rather than collecting new…
Data Provenance Initiative
The Data Provenance Initiative (DPI) is a volunteer-led, multi-institution research collective that audits and documents the licenses, sources, creators, and consent status of the datasets most widely used to…
Data Set or Dataset
A dataset (also written as "data set") is a structured collection of data points used to train, validate, and evaluate machine learning models.
Data preprocessing
Data preprocessing is the set of operations applied to raw data to clean and transform it into a form a machine learning model can use, covering deduplication, type fixing, missing-value imputation, outlier…
Dense Feature
A dense feature is a feature in machine learning whose vector representation consists mostly or entirely of non-zero values, typically stored as a dense numeric array (a vector or tensor) of floating-point…
Derived label
A derived label is a label that has been generated programmatically or inferred from other observable signals, rather than collected from direct human annotation of the variable a model is trying to predict.
Dimension Reduction
Dimensionality reduction, also known as dimension reduction, is the process of transforming data from a high-dimensional space into a lower-dimensional space while retaining as much of the meaningful structure…
Discrete Feature
A discrete feature is a feature (a variable in a dataset) that takes one of a finite or countably infinite set of distinct values, such as a category or an integer count
Distribution shift
Distribution shift is the condition in which the probability distribution that produced a model's training data differs from the distribution that produces the data the model actually encounters at test or…
Downsampling
Downsampling is the process of reducing the number of samples, the spatial resolution, or the number of data instances in a signal, image, or dataset in order to lower computational cost and memory use while…
Feature
In machine learning and statistics, a feature is an individual measurable property or characteristic of a phenomenon being observed, used as an input variable from which a model learns patterns and makes…
Feature Cross
A feature cross (also called a crossed feature or feature interaction) is a synthetic feature created by combining two or more existing features so that a model can learn their joint effect on a prediction.
Feature Engineering
Feature engineering is the process of using domain knowledge to create, transform, and select features from raw data so that machine learning models can learn more effectively.
Feature Extraction
Feature extraction is the process of transforming raw data into a smaller set of derived, informative numerical variables called features that capture the essential structure of the input while discarding…
Feature Selection
Feature selection is the process of choosing a subset of the most relevant input variables (features) from a larger candidate pool for use in a machine learning model, with the goal of finding the smallest set…
Feature Set
A feature set is the complete collection of input variables (features, attributes, or predictors) that a machine learning model uses to learn patterns and make predictions.
Feature Vector
A feature vector is an n-dimensional, ordered list of numerical values that represents the measurable properties of an object, data point, or observation in a format suitable for processing by machine learning…
FineWeb
FineWeb is a large-scale, open pretraining dataset for large language models (LLMs) created by Hugging Face.
FineWeb-2
FineWeb-2 (also written FineWeb2) is a massively multilingual web pretraining dataset released by Hugging Face in December 2024.
FineWeb-Edu
FineWeb-Edu is an open, English-language pretraining dataset of roughly 1.3 trillion tokens
Ground Truth
Ground truth is verified, correct information that serves as the authoritative reference for training and evaluating machine learning models.
Imbalanced Dataset
An imbalanced dataset is a dataset used in machine learning where the classification categories are not approximately equally represented, so that one class (the majority class) contains far more samples than…
Instance
In machine learning and statistics, an instance is a single data point in a dataset: the values of one row of features and, optionally, a label .
Iris dataset
The Iris dataset, sometimes referred to as Fisher's Iris dataset or the Iris flower dataset, is a multivariate dataset introduced by the British statistician and biologist Ronald Fisher in his 1936 paper "The…
LAION
LAION (Large-scale Artificial Intelligence Open Network) is a German non-profit organization
Label
In machine learning, a label is the target output value associated with a single training example: the correct answer that a supervised learning model is trained to predict.
Low-Resource Languages
Low-resource languages, in natural language processing (NLP), are languages with little of the raw material that modern language technology depends on: digitized text, parallel translation corpora, annotated…
MNIST
The Modified National Institute of Standards and Technology (MNIST) database is a collection of 70,000 grayscale images of handwritten digits (0 through 9) that has served as one of the most widely used…
Noise
Noise in machine learning is any unwanted, irrelevant, or random variation in data that obscures the true underlying patterns a model is trying to learn.
Non-Response Bias
Non-response bias is the error that arises when the people or units that do not respond to a survey, study, or data collection process differ systematically from those that do
Normalization
Normalization is the process of scaling numerical data to a standard range or distribution so that features and activations are comparable and downstream computation behaves predictably.
Numerical Data
Numerical data (also called quantitative data) is information expressed as numbers on a continuous or discrete scale that supports arithmetic operations such as addition, subtraction, multiplication, and…
One-Hot Encoding
One-hot encoding is a data preprocessing technique that converts a categorical variable with $$k$$ distinct categories into $$k$$ binary columns, where each category is represented by a vector that contains…
OpenThoughts
OpenThoughts is an open-source initiative and a series of datasets of verified reasoning traces created to train open reasoning models.
Outlier Detection
Outlier detection is the process of identifying data points, observations, or patterns that deviate so markedly from the rest of a dataset that they are likely to have been generated by a different process.
Oversampling
Oversampling is a data preprocessing technique in machine learning that fixes class imbalance by increasing the number of minority class examples in the training set, either by duplicating existing samples…
PASCAL VOC
PASCAL VOC (Pattern Analysis, Statistical Modelling and Computational Learning Visual Object Classes) is a long-running benchmark dataset and annual challenge for object recognition, object detection…
Participation Bias
Participation bias is a systematic error that arises when the individuals who choose to take part in a study, survey, or data collection effort differ in meaningful ways from those who do not, so the resulting…
Preprocessing
Preprocessing is the stage of a machine learning workflow that transforms raw data into a clean, consistent, numerical format that learning algorithms can use.
Proxy labels
A proxy label (also called a surrogate label) is an observable, easy-to-collect stand-in for the true target a model should predict, used to train that model when the real target is too expensive, too slow, or…
Quantile bucketing
Quantile bucketing, also called quantile binning, equal-frequency binning, or quantile discretization
RedPajama
RedPajama is a family of large-scale, openly licensed datasets for training large language models (LLMs), created by Together AI with academic and open-source partners to reproduce, in fully open form
Reporting Bias
Reporting bias is a type of data bias in machine learning that occurs when the frequency of events, properties, or outcomes captured in a dataset does not reflect their real-world frequency, because people…
Sampling Bias
Sampling bias is a systematic error in statistics and machine learning that occurs when a sample is collected so that some members of the intended population have a higher or lower probability of being…
Scaling
In machine learning and artificial intelligence, scaling is an umbrella term for several distinct practices: (1) feature scaling, the data-preprocessing step that rescales input features to a common range so…
Selection Bias
Selection bias is a systematic error that occurs when the data used for analysis, training, or evaluation does not accurately represent the population or domain it is intended to describe
Snorkel
Snorkel is an open-source software framework and methodology for programmatic data labeling that started at Stanford University in 2015 and is licensed under Apache 2.0 .
Sparse Feature
A sparse feature is a feature in machine learning whose values are predominantly zero or empty across a dataset.
Sparse Representation
Sparse representation is a data encoding strategy in which most values in a vector, matrix, or tensor are zero (or near zero), with only a small fraction of elements carrying non-zero values.
Synthetic Feature
A synthetic feature (also called a constructed feature or derived feature) is a variable created by transforming, combining, or otherwise manipulating one or more existing features in a dataset, rather than…
The Pile (dataset)
The Pile is an 825.18 GiB (approximately 886 GB) English text corpus designed for training large language models, assembled from 22 diverse, high-quality subsets spanning academic, professional, internet…
The Stack v2
The Stack v2 is a large open dataset of source code released by BigCode in February 2024 as the training dataset behind the StarCoder2 family of code models.