In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model 's learning process.

Nov 8, 2025 · Hyperparameter tuning is the process of selecting the optimal values for a machine learning model's hyperparameters. These are typically set before the actual training process …

Apr 11, 2025 · Unlike the model’s internal parameters (the weights and biases learned during training), hyperparameters are set before the training process begins. They govern the …

Dec 30, 2020 · So what exactly are parameters and hyperparameters and how do they relate? Hyperparameters are parameters whose values control the learning process and determine …

Nov 29, 2024 · Hyperparameters are high-level settings that control how a model learns. Think of them like the dials on an old-school radio—just as you tune a station for clarity, …

Apr 30, 2025 · Explore the role hyperparameters play in designing effective machine learning models. Hyperparameters are the configuration settings you define before training your …

Hyperparameters are external configuration variables that data scientists set before training a machine learning model. They control the learning process but do not learn from the data. …

Apr 24, 2025 · Summary: Hyperparameters are external parameters set before training a Machine Learning model, such as learning rate, number of trees, or batch size. Unlike model …

Hyperparameters, on the other hand, are not learned parameters. They’re static. They’re the knobs and dials you set before the model starts learning. They don’t come from the data—they …

In machine learning, hyperparameters are the parameters that are set before the learning process begins. Unlike model parameters that are learned during the training, hyperparameters need …