فهرست مطالب

• Cover
• Title Page
• Copyright and Credits
• Contributors
• Table of Contents
• Preface
• Section 1: The Methods
• Chapter 1: Evaluating Machine Learning Models
• Technical requirements
• Understanding the concept of overfitting
• Creating training, validation, and test sets
• Exploring random and stratified splits
• Discovering repeated k-fold cross-validation
• Discovering Leave-One-Out cross-validation
• Discovering LPO cross-validation
• Discovering time-series cross-validation
• Summary
• Further reading
• Chapter 2: Introducing Hyperparameter Tuning
• What is hyperparameter tuning?
• Demystifying hyperparameters versus parameters
• Understanding hyperparameter space and distributions
• Summary
• Chapter 3: Exploring Exhaustive Search
• Understanding manual search
• Understanding grid search
• Understanding random search
• Summary
• Chapter 4: Exploring Bayesian Optimization
• Introducing BO
• Understanding BO GP
• Understanding SMAC
• Understanding TPE
• Understanding Metis
• Summary
• Chapter 5: Exploring Heuristic Search
• Understanding simulated annealing
• Understanding genetic algorithms
• Understanding particle swarm optimization
• Understanding Population-Based Training
• Summary
• Chapter 6: Exploring Multi-Fidelity Optimization
• Introducing MFO
• Understanding coarse-to-fine search
• Understanding successive halving
• Understanding hyper band
• Understanding BOHB
• Summary
• Section 2: The Implementation
• Chapter 7: Hyperparameter Tuning via Scikit
• Technical requirements
• Introducing Scikit
• Implementing Grid Search
• Implementing Random Search
• Implementing Coarse-to-Fine Search
• Implementing Successive Halving
• Implementing Hyper Band
• Implementing Bayesian Optimization Gaussian Process
• Implementing Bayesian Optimization Random Forest
• Implementing Bayesian Optimization Gradient Boosted Trees
• Summary
• Chapter 8: Hyperparameter Tuning via Hyperopt
• Technical requirements
• Introducing Hyperopt
• Implementing Random Search
• Implementing Tree-structured Parzen Estimators
• Implementing Adaptive TPE
• Implementing simulated annealing
• Summary
• Chapter 9: Hyperparameter Tuning via Optuna
• Technical requirements
• Introducing Optuna
• Implementing TPE
• Implementing Random Search
• Implementing Grid Search
• Implementing Simulated Annealing
• Implementing Successive Halving
• Implementing Hyperband
• Summary
• Chapter 10: Advanced Hyperparameter Tuning with DEAP and Microsoft NNI
• Technical requirements
• Introducing DEAP
• Implementing the Genetic Algorithm
• Implementing Particle Swarm Optimization
• Introducing Microsoft NNI
• Implementing Grid Search
• Implementing Random Search
• Implementing Tree-structured Parzen Estimators
• Implementing Sequential Model Algorithm Configuration
• Implementing Bayesian Optimization Gaussian Process
• Implementing Metis
• Implementing Simulated Annealing
• Implementing Hyper Band
• Implementing Bayesian Optimization Hyper Band
• Implementing Population-Based Training
• Summary
• Section 3: Putting Things into Practice
• Chapter 11: Understanding the Hyperparameters of Popular Algorithms
• Exploring Random Forest hyperparameters
• Exploring XGBoost hyperparameters
• Exploring LightGBM hyperparameters
• Exploring CatBoost hyperparameters
• Exploring SVM hyperparameters
• Exploring artificial neural network hyperparameters
• Summary
• Chapter 12: Introducing Hyperparameter Tuning Decision Map
• Getting familiar with HTDM
• Case study 1 – using HTDM with a CatBoost classifier
• Case study 2 – using HTDM with a conditional hyperparameter space
• Case study 3 – using HTDM with prior knowledge of the hyperparameter values
• Summary
• Chapter 13: Tracking Hyperparameter Tuning Experiments
• Technical requirements
• Revisiting the usual practices
• Exploring Neptune
• Exploring scikit-optimize
• Exploring Optuna
• Exploring Microsoft NNI
• Exploring MLflow
• Summary
• Chapter 14: Conclusions and Next Steps
• Revisiting hyperparameter tuning methods and packages
• Revisiting HTDM
• What’s next?
• Summary
• Index
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