Machine Learning Hyperparameter Optimization

This repository contains code and discussions related to hyperparameter optimization (HPO) for machine learning models. The focus is on implementing and comparing different HPO techniques using Python and popular libraries like scikit-learn, Optuna, and HyperOpt.

Contents

1. Overview
2. Techniques Used
3. Folder Structure
4. Instructions

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1. Overview

In this repository, we explore various methods of hyperparameter optimization to improve the performance of machine learning models. The goal is to find the optimal set of hyperparameters that maximize the model's accuracy or other relevant metrics. We discuss and implement the following techniques:

• Random Search: A baseline approach where hyperparameters are randomly sampled from predefined ranges.

• Bayesian Optimization: An iterative optimization technique that uses probabilistic models to find the optimal hyperparameters based on past evaluations.

• HyperOpt: A Python library for optimizing machine learning model parameters using Tree-structured Parzen Estimator (TPE) and other algorithms.

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2. Techniques Used

Random Search

Random Search is implemented to randomly sample hyperparameters from predefined distributions and evaluate their performance using cross-validation or hold-out validation.

Bayesian Optimization

Bayesian Optimization utilizes probabilistic models (such as Gaussian Processes) to model the objective function (model performance) and iteratively select the next set of hyperparameters that are likely to improve performance.

HyperOpt

HyperOpt employs algorithms like Tree-structured Parzen Estimator (TPE) to efficiently search through the hyperparameter space by leveraging past evaluations to guide the search.

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3. Folder Structure

• random_search/: Contains scripts and notebooks for implementing Random Search.
• bayesian_optimization/: Includes code and notebooks for implementing Bayesian Optimization.
• hyperopt/: Code and notebooks for implementing HyperOpt.

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4. Instructions

Setup

1. Clone the repository:

   git clone <repo_url>
   cd <repo_name>

2. Set up a virtual environment (optional but recommended):

   python -m venv env
   source env/bin/activate  # On Windows use `env\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

Usage

• Navigate to the respective folders (random_search, bayesian_optimization, hyperopt) to run scripts or notebooks related to each technique.
• Follow the instructions provided within each folder or script for executing and evaluating hyperparameter optimization.

Results and Visualizations

• Each folder may include visualizations and results of hyperparameter optimization techniques.
• Use the provided visualizations to compare performance metrics and hyperparameter distributions across different methods.

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Conclusion

This repository serves as a practical guide and implementation of various hyperparameter optimization techniques in machine learning.

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