Mastering SGDRegressor: How to Stop Training After a Fixed Number of Epochs

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When working with machine learning models, controlling how long your model trains is crucial. Train too little, and the model underfits; train too long, and it may overfit or waste computational resources.

In Scikit-Learn’s SGDRegressor, one common question is:

👉 How do we make the model stop after exactly 1000 epochs (iterations)?

The Question

We’re given multiple options for initializing SGDRegressor:

from sklearn.linear_model import SGDRegressor

# Option 1 ❌
linear_regressor = SGDRegressor(max_epoch=1000)

# Option 2 ❌
linear_regressor = SGDRegressor(stopping_criteria=1000)

# Option 3 ✅
linear_regressor = SGDRegressor(max_iter=1000)

# Option 4 ❌
linear_regressor = SGDRegressor(stop_after_iter=1000)

Only Option 3 is correct. Let’s understand why.

The Correct Way

In Scikit-Learn, the parameter that controls the maximum number of iterations (epochs) for training is:

SGDRegressor(max_iter=1000)

Here:

  • max_iter → The maximum number of passes (epochs) over the training dataset.

  • Default value is usually 1000 but can be explicitly set.

  • Along with max_iter, you may also see tol (tolerance) being used. If tol is set, training may stop early when convergence is detected. If you want exactly 1000 epochs, you can disable early stopping by setting tol=None.

Example:

linear_regressor = SGDRegressor(max_iter=1000, tol=None)
linear_regressor.fit(X, y)

This ensures the model runs exactly 1000 iterations regardless of convergence.

Why Not the Other Options?

  • max_epoch ❌: This parameter does not exist in Scikit-Learn.

  • stopping_criteria ❌: Also invalid, Scikit-Learn uses tol for early stopping, not stopping_criteria.

  • stop_after_iter ❌: Another non-existent parameter.

Only max_iter is officially supported.

Key Takeaways

  • To control training length in SGDRegressor, always use max_iter.

  • Set tol=None if you want exact iterations without early stopping.

  • Understanding the right parameters saves debugging time and ensures you fully control the learning process.

✅ Final Answer:
To make SGDRegressor stop after 1000 epochs, use:

linear_regressor = SGDRegressor(max_iter=1000)

Would you like me to also extend this blog with a real-world code demo (using a dataset like Boston Housing) so readers see how the model behaves with and without tol?

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