Hyperparameter Tuning
Hyperparameter tuning is the process of systematically searching for the configuration values that control a machine learning model's training process — such as learning rate or tree depth — that produce the best performance on a given task.
12 resources across 3 libraries
Glossary Terms(10)
Feature Engineering
Feature engineering is the process of using domain knowledge to select, transform, and create the input variables (features) that a machine learning model is t…
Model Drift
Model drift (also called concept drift) is the gradual decline in a machine learning model's predictive performance over time, occurring because the real-world…
Cross-Validation
Cross-validation is a model evaluation technique that repeatedly splits a dataset into different training and validation subsets, trains and tests the model on…
Hyperparameter Tuning
Hyperparameter tuning is the process of systematically searching for the configuration values that control a machine learning model's training process — such a…
AutoML
AutoML (Automated Machine Learning) refers to tools and techniques that automate parts of the machine learning pipeline — such as feature selection, model sele…
Neural Architecture Search
Neural Architecture Search (NAS) is an automated technique for designing effective neural network architectures by systematically searching a space of possible…
Early Stopping
Early stopping is a regularization technique that halts model training once performance on a held-out validation set stops improving, preventing the model from…
Grid Search
Grid search is a hyperparameter tuning method that exhaustively evaluates a model across every combination of hyperparameter values from a predefined discrete…
Random Search
Random search is a hyperparameter tuning method that samples a fixed number of hyperparameter combinations randomly from specified distributions or ranges, rat…
Bayesian Optimization
Bayesian optimization is a sample-efficient hyperparameter tuning method that builds a probabilistic surrogate model of how hyperparameters affect performance,…