Hyperparameter Tuning: 100 Tips & Insights for Model Fine-Tuning

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Hyperparameter tuning is a crucial aspect of optimizing machine learning models for better performance. Here are 100 tips and tricks for hyperparameter tuning:

1. General Tips:

1. Understand the problem: Have a clear understanding of the problem and the characteristics of the data.
2. Define objectives: Clearly define what you want to achieve with hyperparameter tuning.
3. Use a validation set: Split your data into training, validation, and test sets to evaluate model performance.
4. Start with default values: Use default hyperparameter values provided by the library/framework as a baseline.
5. Random search: Consider using random search before grid search for a more efficient exploration of hyperparameter space.
6. Use appropriate metrics: Choose evaluation metrics that align with your problem (e.g., accuracy, F1 score, ROC-AUC).
7. Normalize/Standardize data: Scaling input features can impact hyperparameter sensitivity, so preprocess your data accordingly.
8. Feature engineering: Create relevant features to improve model performance and reduce the need for complex models.
9. Use appropriate algorithms: Different…