Hyperparameter tuning with Python boost your machine learning model's performance via hyperparameter tuning
This book curates numerous hyperparameter tuning methods for Python all in one place, providing a deep explanation of how each method works, and a decision map that can help you choose which hyperparameter tuning method is right for your specific problem and situation.
| Other Authors: | |
|---|---|
| Format: | eBook |
| Language: | Inglés |
| Published: |
Birmingham, England :
Packt Publishing
[2022]
|
| Subjects: | |
| See on Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009672597406719 |
Table of Contents:
- Table of Contents Evaluating Machine Learning Models Introducing Hyperparameter Tuning Exploring Exhaustive Search Exploring Bayesian Optimization Exploring Heuristic Search Exploring Multi-Fidelity Optimization Hyperparameter Tuning via Scikit Hyperparameter Tuning via Hyperopt Hyperparameter Tuning via Optuna Advanced Hyperparameter Tuning with DEAP and Microsoft NNI Understanding Hyperparameters of Popular Algorithms Introducing Hyperparameter Tuning Decision Map Tracking Hyperparameter Tuning Experiments Conclusions and Next Steps.
