AI Terms Dictionary

Definition

Tuning involves refining a machine learning model to achieve better accuracy or efficiency. It can refer to hyperparameter tuning, where settings like learning rate or batch size are optimized, or fine-tuning, where pre-trained model weights are updated on a target dataset. Effective tuning balances bias and variance, ensuring the model generalizes well to unseen data without overfitting to the training set.

Summary

The process of adjusting hyperparameters or model weights to optimize performance on a specific dataset or task.

Key Concepts

• Hyperparameters
• Grid Search
• Random Search
• Overfitting Prevention

Use Cases

• Optimizing model accuracy
• Reducing inference latency
• Adapting models to specific domains

Code Example

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from sklearn.model_selection import GridSearchCV
param_grid = {'C': [0.1, 1, 10]}
search = GridSearchCV(svm, param_grid, cv=5)

Related Terms

• hyperparameter_optimization
• grid_search
• fine_tuning
• validation