Hyperparameter Tuning
The process of finding the optimal hyperparameter values for a machine learning model.
In-depth explanation
Hyperparameter tuning searches the space of possible hyperparameter combinations to find the best configuration. Methods include grid search (exhaustive search), random search (random sampling), and Bayesian optimization (intelligent search based on past results). Automated tools like Optuna and Ray Tune help streamline this process.
Examples
Related terms
More in Machine Learning
Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
Unsupervised Learning
Machine learning approach where models find patterns in data without labeled examples.
Semi-Supervised Learning
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Regression
Predicting a continuous numerical value based on input features.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
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