Machine Learning Hyperparameter Optimization Project

Welcome to my Machine Learning Hyperparameter Optimization project! In this project, we explore various techniques for optimizing hyperparameters to improve the performance of machine learning models. I specifically focused on Genetic Algorithms and Simulated Annealing as optimization methods, and I evaluated their performance using cross-validation.

Introduction

Hyperparameter tuning is a critical step in the machine learning model development process. Properly configured hyperparameters can significantly impact a model's performance. In this project, we investigate two popular optimization techniques: Genetic Algorithms and Simulated Annealing.

Optimization Techniques

Genetic Algorithms

Genetic Algorithms (GAs) are inspired by the process of natural selection. GAs evolve a population of potential solutions over several generations to find an optimal combination of hyperparameters for our machine learning models. Our implementation of GAs explores different hyperparameter combinations, selects the best individuals using tournament selection, and creates a new generation through crossover and mutation.

Simulated Annealing

Simulated Annealing is a stochastic optimization algorithm inspired by the annealing process in metallurgy. Instead of a fixed temperature, we use probabilistic teperature-like parameter as the "temperature" to accept or reject new hyperparameter combinations. Simulated Annealing explores the hyperparameter space while gradually reducing the "temperature," allowing it to escape local optima and potentially find a global optimum.

Performance Evaluation

To assess the performance of Genetic Algorithms, Simulated Annealing, and GridSearchCV (a commonly used hyperparameter optimization technique), we employ cross-validation. Cross-validation helps us measure the stability and generalization of each optimization method across different subsets of our dataset.

We compare the performance of these three methods in terms of both computational efficiency and the quality of hyperparameter configurations they discover. Our goal is to identify which technique yields the best model performance within the given computational resources.