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Introduction
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Welcome to SKSurrogate’s documentation!
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Contents:
Introduction
Dependencies
Download
Installation
Documentation
License
MIT License
Surrogate-Based Optimization
Sampling
CompactSample
BoxSample
SphereSample
Surrogate models
Optimizer
Hyperparameter Optimization
Hilbert Spaces
Orthonormal system of functions
Sensitivity Analysis
Morris
Sobol
Moment-Independent
\(\delta\)
Index
Eliminate features based on Pearson correlation
Evolutionary Optimization Algorithms
A General Evolutionary Optimization Algorithm
Example
Optimized Pipeline Detector
Some Technical Notes
Stacking
Permutation Importance
imblearn pipelines
Categorical Variables
A machine learning progress tracker
SVM Classifier with RBF v.s. SGDClassifier with Kernels
Code Documentation
Surrogate Random Search
Evolutionary Optimization Algorithm
Hilbert Space based regression
Sensitivity Analysis
Optimized Pipeline Detector
MLTrace: A machine learning progress tracker
synthdata
Module
Supported data types
Generating Synthetic Data
Constraints
Code documentation
Indices and tables
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Index
Module Index
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SKSurrogate 0.1.0 documentation
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