Data Science

Active Learning is a machine learning approach where the model selects the data it learns from to improve performance.

AI in Science refers to the application of artificial intelligence technologies to enhance scientific research and discovery.

Algorithm selection is the process of choosing the most suitable algorithm for a specific problem or dataset.

Algorithmic fairness ensures that algorithms treat individuals and groups equitably, minimizing bias and discrimination.

Anomaly Detection is the identification of patterns in data that do not conform to expected behavior.

Approximate nearest neighbors (ANN) are algorithms that quickly find points in a dataset that are closest to a given query point.

Approximation error measures the difference between an estimated value and the actual value.

Artificial Intelligence (AI) refers to computer systems designed to perform tasks that typically require human intelligence.

Automated Machine Learning (AutoML) simplifies the process of building machine learning models by automating key tasks.

AutoML (Automated Machine Learning) simplifies the process of applying machine learning by automating tasks traditionally done by data scientists.

An AutoML Pipeline automates the process of building and optimizing machine learning models.

Azure Machine Learning is a cloud-based service for building, training, and deploying machine learning models.

A Bayesian Network is a graphical model representing probabilistic relationships among variables.

Behavior informatics is the study of data related to human behavior using computational methods.

A benchmark dataset is a standard set of data used to evaluate the performance of machine learning models.

Bias in AI refers to systematic errors in algorithms that lead to unfair outcomes based on attributes like race or gender.

Big Data Analytics involves examining large datasets to uncover patterns and insights for better decision-making.

Calibration is the process of adjusting a system to ensure its outputs are accurate and reliable.

CatBoost is a machine learning algorithm that uses gradient boosting on decision trees, designed for categorical features.

A categorical variable represents distinct categories or groups within data, often used in statistical analysis.

A centrality measure quantifies the importance of nodes in a network.

Churn Prediction is a technique used to identify customers likely to stop using a service.

Class imbalance occurs when the classes in a dataset are not represented equally, affecting model performance.

ClearML is an open-source platform for managing machine learning experiments, pipelines, and models.

Client Drift refers to the phenomenon where a model's performance declines due to changes in client data over time.

A collection of programming contests and solutions used for AI and algorithm training.

A cold start refers to the challenge of making accurate predictions or recommendations when there's little or no data available.

Common Crawl is a non-profit organization that provides a free, open archive of web data for research and analysis.