#### Overview of this book

Preface
Section 1:The Methods
Free Chapter
Chapter 1: Evaluating Machine Learning Models
Chapter 2: Introducing Hyperparameter Tuning
Chapter 3: Exploring Exhaustive Search
Chapter 4: Exploring Bayesian Optimization
Chapter 5: Exploring Heuristic Search
Chapter 6: Exploring Multi-Fidelity Optimization
Section 2:The Implementation
Chapter 7: Hyperparameter Tuning via Scikit
Chapter 8: Hyperparameter Tuning via Hyperopt
Chapter 9: Hyperparameter Tuning via Optuna
Chapter 10: Advanced Hyperparameter Tuning with DEAP and Microsoft NNI
Section 3:Putting Things into Practice
Chapter 11: Understanding the Hyperparameters of Popular Algorithms
Chapter 12: Introducing Hyperparameter Tuning Decision Map
Chapter 13: Tracking Hyperparameter Tuning Experiments
Chapter 14: Conclusions and Next Steps
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# Case study 2 – using HTDM with a conditional hyperparameter space

Let’s say we are faced with a similar condition as in the previous section but now, we are working with a conditional hyperparameter space, as defined here:

`one_hot_max_size = randint(2,15)`
`iterations = randint(5,200)`
`If iterations < 50:`
`   depth = randint(3,10)`
`   learning_rate = np.linspace(5e-4,1e-3,10)`
`   l2_leaf_reg = np.linspace(1,15,20)`
`elif iterations < 100:`
`   depth = randint(3,7)`
`   learning_rate = np.linspace(1e-5,5e-4,10)`
`l2_leaf_reg = np.linspace(5,20,20)`
`else:`
`     depth = randint(3,5)`
`     learning_rate = np.linspace(1e-6,5e-5,10)`
`l2_leaf_reg = np.linspace(5,30,20)`

Based on the given case description, we can try to utilize HTDM again to help us choose which hyperparameter tuning method suits the condition the best....