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Model parameters are values a model learns from data; hyperparameters are choices that configure the model or the way it learns. A weight or bias helps determine a prediction. A learning rate, batch size, or number of training epochs helps determine how training finds those learned values.
What are model parameters?
Parameters are internal values fitted during training. In many models, they include weights and biases. Once learned, those values are used to calculate the model’s predictions. Google’s Machine Learning Glossary describes them as “the various weights and bias that the model learns during training.”
For a simple linear model, a weight (or coefficient) determines how strongly an input contributes to the prediction, while a bias (or intercept) provides an offset. Training uses data to estimate or update these values.
What are hyperparameters?
Hyperparameters are settings selected to shape a model or its training rather than being learned as the model’s ordinary weights and biases. Common examples include the learning rate, batch size, epoch count, optimizer, regularization settings, and some architecture choices. The role a setting plays depends on the learning method and on what an experiment is trying to compare.
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Parameters and hyperparameters at a glance
| Item | Typical role | What it does |
|---|---|---|
| Weight or coefficient | Model parameter | A learned value used to calculate predictions. |
| Bias or intercept | Model parameter | A learned offset in a prediction function. |
| Learning rate | Training hyperparameter | Controls the scale of parameter updates. |
| Batch size | Training hyperparameter | Sets how many examples are processed before an update to the model’s weights and bias. |
| Epoch count | Training hyperparameter | Sets how many times training processes the full dataset. |
| Optimizer choice | Often a training hyperparameter | Specifies an approach for updating model parameters. |
| Number of layers | Often an architectural or experimental hyperparameter | Changes the model architecture; its classification depends on the experiment. |
How the distinction works during training
Think of training as a process that uses data and a chosen training setup to adjust parameters. In a gradient-descent example, the learning rate influences how large an update is; the batch size determines how many examples contribute before an update; and the epoch count sets how many passes are made through the training examples. The values being updated—the weights and bias—are parameters. Google explains these settings in its linear-regression hyperparameters lesson.
This is a distinction of role, not of whether a person or program can change a value. Practitioners may tune hyperparameters, and automated tuning software can search for them. Parameters, meanwhile, are updated or estimated from data during model fitting.
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Why hyperparameters cannot always be tuned one at a time
Hyperparameters can interact. For example, batch size can affect which optimizer and regularization settings work well. Changing batch size while leaving the rest of the training pipeline untouched can make a comparison misleading; the Google Deep Learning Tuning Playbook FAQ discusses this interaction.
There is no universally best learning rate: the appropriate value depends on the model and dataset, as Google’s instructional material notes. More broadly, a model comparison should begin with the question being tested. If the question is whether one architecture performs better, hold unrelated settings constant where appropriate, or retune them fairly so they do not distort the result. Architecture choices can also affect training speed, memory use, serving cost, and latency. The Playbook’s scientific approach explains how settings may be treated as scientific, nuisance, fixed, or conditional hyperparameters depending on the experiment.
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In everyday deep-learning usage, “hyperparameter” often broadly means a setting chosen for training or model design. In Bayesian machine learning, the term has a more precise meaning, so the broad usage can be ambiguous. The Google Deep Learning Tuning Playbook FAQ notes that “metaparameter” may be used in research writing to avoid that ambiguity, while “hyperparameter” remains common for a general audience.
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