What is Hyperparameter Optimization

What is Hyperparameter Optimization?

Hyperparameter Optimization (also called Hyperparameter Tuning) is the process of selecting the best set of hyperparameters for a machine learning model to improve its performance.

What Are Hyperparameters?

• Hyperparameters are parameters set before the learning process begins.

• They are not learned from the data — unlike model parameters like weights or coefficients.

Why Is It Important?

The choice of hyperparameters significantly affects a model’s:

• Accuracy

• Training time

• Generalization on unseen data

Poor tuning can lead to overfitting or underfitting.

Tools That Help

• Optuna

• Hyperopt

• Ray Tune

• Scikit-learn’s GridSearchCV / RandomizedSearchCV

• Keras Tuner

• AutoML platforms (Google AutoML, H2O.ai, Azure AutoML)