Concept graph
Generative AI and LLM glossary
Clear AI definitions with practical context, examples, and links to related models, tools, and concepts.
Data Preparation
Feature Engineering
The process of selecting, modifying, or creating features from raw data.
Model Interpretation
Feature Importance
A measure of how much a feature contributes to the predictive power of a model.
Machine Learning
Feature Mapping
The process of transforming input features into a more suitable format for modeling.
Data Preprocessing
Feature Scaling
The process of standardizing or normalizing features so they contribute equally to the model.
Machine Learning
Feature Vector
A numerical representation of an object's characteristics used in machine learning.
Data Processing
feature-extraction
The process of transforming raw data into a set of usable characteristics for model training.
Data Processing
feature-selection
The process of selecting a subset of relevant features for model training.
Machine Learning
Federated Learning
A machine learning approach that allows models to be trained across decentralized devices or servers holding local data samples.
Machine Learning
few-shot-learning
A machine learning paradigm that trains models with very few labeled examples.
Machine Learning
fine-tuning
The process of adjusting a pre-trained model on a new, often smaller dataset to improve performance on a specific task.