Condition
In machine learning, a condition is any node in a decision tree that performs a test on one or more features and routes an example to one of its child nodes based on the result .
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Conditional Random Field
A conditional random field (CRF) is a discriminative probabilistic model for structured prediction, used most often to assign a label to every position in an input sequence.
Confident Learning (CL)
Confident Learning (CL) is a data-centric machine learning framework for characterizing, finding, and learning with label errors in datasets.
Confirmation Bias
Confirmation bias is the tendency to search for, interpret, favor, and recall information in ways that confirm one's preexisting beliefs, and in artificial intelligence it appears in three main forms: human…
Confusion Matrix
A confusion matrix is a table that summarizes the performance of a classification model by tabulating its predicted class labels against the actual class labels, with correct predictions on the diagonal and…
Connectionist Temporal Classification
Connectionist temporal classification (CTC) is a loss function and output layer design for training neural networks to label unsegmented sequences, such as transcribing an audio recording into characters when…
Context window
A context window is the finite token sequence that a language model can process for one invocation.
Continual learning
Continual learning, also called lifelong learning or incremental learning, is a machine learning paradigm in which a model learns from a stream of tasks or data distributions over time
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.
Contrastive Learning
Contrastive learning is a family of machine learning methods that learn representations by pulling similar (positive) pairs of data points closer together in an embedding space while pushing dissimilar…
Contrastive decoding
Contrastive decoding (CD) is a decoding strategy for text generation from a large language model that selects tokens by contrasting two models of different sizes.
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…
Convergence
Convergence in machine learning is the point at which an iterative optimization algorithm reaches a stable solution, meaning the loss function stops decreasing meaningfully and further parameter updates yield…
Convex Function
A convex function is a real-valued function whose graph curves upward into a bowl or cup shape, so that the line segment (chord) connecting any two points on the graph lies on or above the graph itself.
Convex Optimization
Convex optimization is the branch of mathematical optimization that minimizes a convex function over a convex set, a problem class with one defining advantage: every local minimum is also a global minimum.
Convex Set
A convex set is a set of points in which the line segment connecting any two points of the set lies entirely within the set .
Convolution
Convolution is a mathematical operation that combines two functions to produce a third function, expressing how the shape of one is modified by the other.
Convolutional Filter
A convolutional filter (also called a kernel or feature detector) is a small matrix of learnable weights that slides across an input and computes a dot product at each position to produce a feature map.
Convolutional Layer
A convolutional layer is the core building block of a convolutional neural network (CNN): it slides a small set of learnable filters (also called kernels) across the input, computing a convolution (technically…
Convolutional Neural Network
A convolutional neural network (CNN, or ConvNet) is a neural network that uses convolution-like linear operators in at least some layers.
Convolutional Operation
The convolutional operation is a mathematical procedure that combines two functions to produce a third function expressing how the shape of one is modified by the other.
Coreference Resolution
Coreference resolution is the natural language processing task of finding all the expressions in a text that refer to the same entity and grouping them together.
Corrective RAG (CRAG)
Corrective Retrieval Augmented Generation (CRAG) is a method for improving the robustness of retrieval-augmented generation (RAG) when the underlying retrieval step returns irrelevant, incomplete, or factually…
Cosine similarity
Cosine similarity is a measure of similarity between two non-zero vectors that calculates the cosine of the angle between them, defined as the dot product of the vectors divided by the product of their…
Cost-sensitive learning
Cost-sensitive learning is a family of machine learning methods that minimise the expected misclassification cost rather than the misclassification rate
Counterfactual Fairness
Counterfactual fairness is a formal definition of algorithmic fairness rooted in causal inference: a prediction is counterfactually fair toward an individual if it would remain unchanged in a counterfactual…
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
Crash Blossom
A crash blossom is a newspaper headline that is unintentionally ambiguous because its compressed wording allows more than one valid parse, producing an unintended (and often comical) alternative reading.
Critic
A critic in reinforcement learning (RL) is the component of an actor-critic system that estimates a value function, scoring how good the actor's chosen actions are and converting that judgment into a…
Cross-Entropy
Cross-entropy is a measure from information theory of how many bits (or nats) are needed to encode data drawn from a true probability distribution P when using a code optimized for a different
Cross-Entropy Loss
Cross-entropy loss is the standard loss function for classification and language modeling, defined as the negative log-probability a model assigns to the correct answer: for a single example its value is…
Cross-Validation
Cross-validation is a statistical resampling technique used in machine learning to estimate how accurately a predictive model will generalize to data it was not trained on.
Curriculum learning
Curriculum learning is a training strategy for machine learning models in which training examples are presented in a meaningful, easy-to-hard order rather than at random
Curse of Dimensionality
The curse of dimensionality is the set of problems that arise when data has a large number of features (dimensions): as dimensions increase, the volume of the space grows exponentially, the available data…
DAPO (Decoupled Clip and Dynamic Sampling Policy Optimization)
DAPO, short for Decoupled Clip and Dynamic sAmpling Policy Optimization, is an open-source reinforcement learning algorithm and training system for large language models, introduced in March 2025 by…
DARE (Drop And REscale)
DARE (Drop And REscale) is a training-free preprocessing technique for model merging that sparsifies the parameter changes introduced by fine-tuning before those changes are combined across several models.
DBSCAN
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm that groups together points packed closely in feature space and labels points in low-density regions…
DDPM
Denoising Diffusion Probabilistic Models (DDPM) are a class of generative model introduced by Jonathan Ho, Ajay Jain, and Pieter Abbeel of UC Berkeley in their June 2020 paper "Denoising Diffusion…
DINO (computer vision)
DINO (self-DIstillation with NO labels) is a family of self-supervised learning methods for computer vision from Meta AI that trains Vision Transformers (ViTs) on unlabeled images and produces general-purpose…
DROP (Discrete Reasoning Over Paragraphs)
DROP** (Discrete Reasoning Over Paragraphs) is a reading comprehension benchmark that requires artificial intelligence systems to perform discrete reasoning operations over textual content.
DSPy
DSPy (short for Declarative Self-improving Python) is an open-source framework, developed at Stanford NLP, for programming rather than prompting large language models (LLMs).
Dask
Dask is an open-source Python library for parallel and distributed computing that scales the familiar APIs of libraries such as NumPy, pandas, and scikit-learn to process larger-than-memory datasets.
Data Analysis
Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.
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 Parallelism
Data parallelism is a distributed training technique in which the same neural network model is replicated across multiple processing units (typically GPUs), each device trains on a different shard of the input…
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 Science
Data science is an interdisciplinary field that uses statistics, programming, and domain expertise to extract knowledge and insights from structured and unstructured data.
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 labeling
Data labeling (also called data annotation) is the process of attaching meaningful tags, labels, or metadata to raw data so that machine learning algorithms can learn from it.
Data poisoning
Data poisoning is a class of adversarial attack in which a malicious actor deliberately corrupts the training data used to build 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…
DataFrame
A DataFrame is a two-dimensional, size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns), in which each column can hold a different data type and arithmetic…
Dataset API (tf.data)
The Dataset API (tf.data) is the high-performance input pipeline framework within TensorFlow for loading, transforming, and delivering data to machine learning models during training and evaluation.
Datasets
In machine learning, a dataset is a structured collection of examples used to fit, tune, and evaluate models, where each example pairs input data (features) with an optional target (label or response).
Decision Boundary
A decision boundary (also called a decision surface) is the hypersurface in feature space that separates the regions a classifier assigns to different classes.
Decision Forest
A decision forest is a family of ensemble learning methods in machine learning that combine many decision trees to produce more accurate and stable predictions than any single tree .
Decision Threshold
A decision threshold (also called a classification threshold or cutoff point) is a value used to convert the continuous probability output of a machine learning classifier into a discrete class label.
Decision Tree
A decision tree is a tree-structured model used in supervised learning. It predicts an outcome by routing an input from a root node through a sequence of tests to a terminal leaf.
Decoder
A decoder is the component of a neural network that turns an internal, compressed, or abstract representation into a desired output, such as a translated sentence, a generated image, a reconstructed input, or…
Decoding strategies
Decoding strategies are the algorithms that select output tokens from a language model's next-token probability distribution during text generation.