What is Hyperparameter Tuning in Azure ML?

Updated May 15, 2026

Short answer

Hyperparameter tuning optimizes model parameters automatically to improve model performance.

Deep explanation

Hyperparameters control how machine learning algorithms learn. Azure ML supports automated hyperparameter tuning using:

Grid search
Random search
Bayesian optimization

Azure ML also supports early termination policies to stop poorly performing experiments and reduce costs.

Real-world example

A logistics company tunes XGBoost hyperparameters to improve shipment delay prediction accuracy.

Common mistakes

Searching excessively large parameter spaces, ignoring overfitting, and not using early termination.

Follow-up questions

What is Bayesian optimization?
Why use early termination policies?
What is the difference between parameters and hyperparameters?

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