AI GlossaryHyperparameterENTRY — Machine Learning
[Machine Learning]
Hyperparameter.
Configuration settings set before training that control the learning process, not learned from data.
In-depth explanation
01Hyperparameters are external configurations that affect how a model learns. Unlike model parameters (learned during training), hyperparameters are set by the practitioner. Examples include learning rate, number of layers, regularization strength, and batch size. Hyperparameter tuning—finding optimal values—significantly impacts model performance.
Examples
02EX. 01
Learning rate = 0.001
EX. 02
Number of trees = 100
EX. 03
Dropout rate = 0.5
More in Machine Learning
0301ClassificationPredicting which category or class an input belongs to from a set of predefined categories.02Cross-ValidationA technique to evaluate model performance by training and testing on different subsets of data.03Ensemble LearningCombining multiple models to produce better predictions than any single model.04FeatureAn individual measurable property or characteristic of data used as input to a machine learning model.05Feature EngineeringThe process of using domain knowledge to create new features that improve model performance.06Gradient DescentAn optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
[NEXT] — APPLY THE CONCEPT
Master Hyperparameter.
Learn how to apply this concept with hands-on projects in our comprehensive AI programs.