Hyperparameter Tuning with Sklearn
24
HPT: sklearn
Hyperparameter Tuning Cookbook
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Preface
Optimization
1
Introduction: Optimization
2
Aircraft Wing Weight Example
3
Introduction to
scipy.optimize
4
Sequential Parameter Optimization: Using
scipy
Optimizers
Numerical Methods
5
Introduction: Numerical Methods
6
Kriging (Gaussian Process Regression)
7
Introduction to spotpython
8
Multi-dimensional Functions
9
Isotropic and Anisotropic Kriging
10
Using
sklearn
Surrogates in
spotpython
11
Sequential Parameter Optimization: Gaussian Process Models
12
Expected Improvement
13
Handling Noise
14
Optimal Computational Budget Allocation in spotpython
15
Kriging with Varying Correlation-p
16
Factorial Variables
17
User-Specified Functions: Extending the Analytical Class
Data-Driven Modeling and Optimization
18
Basic Statistics and Data Analysis
19
Regression
20
Classification
21
Clustering
Machine Learning and AI
22
Machine Learning and Artificial Intelligence
Introduction to Hyperparameter Tuning
23
Hyperparameter Tuning
Hyperparameter Tuning with Sklearn
24
HPT: sklearn
25
HPT: sklearn SVC on Moons Data
26
Step 2: Initialization of the Empty
fun_control
Dictionary
Hyperparameter Tuning with River
27
HPT: River
28
Simplifying Hyperparameter Tuning in Online Machine Learning—The spotRiverGUI
29
river
Hyperparameter Tuning: Hoeffding Tree Regressor with Friedman Drift Data
30
The Friedman Drift Data Set
Hyperparameter Tuning with PyTorch Lightning
31
Basic Lightning Module
32
Details of the Lightning Module Integration in spotpython
33
User Specified Basic Lightning Module With spotpython
34
HPT PyTorch Lightning: Data
35
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set
36
Hyperparameter Tuning with PyTorch Lightning and User Data Sets
37
Hyperparameter Tuning with PyTorch Lightning and User Models
38
Hyperparameter Tuning with PyTorch Lightning: ResNets
39
Neural ODEs
40
Neural ODE Example
41
Physics Informed Neural Networks
42
Hyperparameter Tuning with PyTorch Lightning: Physics Informed Neural Networks
43
Explainable AI with SpotPython and Pytorch
44
HPT PyTorch Lightning Transformer: Introduction
45
Hyperparameter Tuning of a Transformer Network with PyTorch Lightning
46
Saving and Loading
47
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a ResNet Model
48
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning for the Diabetes Data Set Using a User Specified ResNet Model
49
Hyperparameter Tuning with
spotpython
and
PyTorch
Lightning Using a CondNet Model
Appendices
A
Introduction to Jupyter Notebook
B
Git Introduction
C
Python Introduction
D
Documentation of the Sequential Parameter Optimization
E
Datasets
F
Using Slurm
G
Python Package Building
H
Solutions to Selected Exercises
References
Table of contents
24.1
Introduction to sklearn
Hyperparameter Tuning with Sklearn
24
HPT: sklearn
24
HPT: sklearn
24.1
Introduction to sklearn
23
Hyperparameter Tuning
25
HPT: sklearn SVC on Moons Data