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Parameters are values a model learns from data; hyperparameters are choices that shape the model or how it is trained. A model’s weights and bias are parameters. Its learning rate, batch size, and number of training epochs are common hyperparameters.
What is the difference between parameters and hyperparameters?
Parameters are internal fitted values that a trained model uses to make predictions. Training estimates or updates them using data. Hyperparameters configure the model or training process; they are selected or tuned rather than serving as the model’s learned weights.
For example, in a linear model, a weight determines how strongly an input affects the prediction, while a bias (or intercept) supplies an offset. The learning rate controls the scale of updates to those values. Batch size controls how many examples contribute before an update, and epoch count sets how many passes training makes through the dataset. Google’s Machine Learning Glossary describes weights and bias as values the model learns during training.
Which values are parameters, and which are hyperparameters?
| Value | 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 the prediction function. |
| Learning rate | Training hyperparameter | Controls the size of parameter updates. |
| Batch size | Training hyperparameter | Sets how many examples are processed before the model updates its values. |
| Epoch count | Training hyperparameter | Sets how many times training processes the full dataset. |
| Number of layers | Often an architectural or experimental hyperparameter | Defines part of the model architecture; whether it is treated as a hyperparameter depends on the experiment. |
| Optimizer choice | Often a training or experimental hyperparameter | Selects the method used to update parameters. |
The examples are typical, not a universal taxonomy. Other common hyperparameters include regularization settings. Whether a choice belongs in a particular hyperparameter comparison depends on the learning method and the question being studied.
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Can parameters and hyperparameters both be adjusted?
Yes. “Parameter” and “hyperparameter” describe a value’s role, not whether a person can change it. During training, an algorithm updates model parameters. A practitioner can choose hyperparameters, or software can search for them automatically.
There is no universally best learning rate: Google’s linear-regression training material notes that the ideal rate depends on the model and dataset. Hyperparameters can also interact. The Deep Learning Tuning Playbook FAQ explains that batch size can interact with optimizer and regularization settings, so changing batch size alone may not give a fair comparison.
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How should hyperparameters be handled when comparing models?
Start by identifying what the comparison is meant to establish—for instance, whether one architecture performs better. Then decide which settings should be held fixed and which should be tuned fairly for each model. The Playbook’s scientific approach distinguishes scientific, nuisance, fixed, and conditional hyperparameters according to the experiment.
Architecture choices can affect more than predictive performance: they may change training speed, memory use, serving cost, and latency. A comparison that changes architecture but leaves relevant training settings untouched may conflate those effects with the result being investigated.
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A terminology caveat: “hyperparameter” can mean different things
In everyday deep-learning practice, “hyperparameter” is often used broadly for settings such as learning rate and batch size. 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, though “hyperparameter” is familiar to a general audience.
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