This repository contains files related to a hyperparameter optimization project. The goal of this project is to optimize the hyperparameters of a machine learning model using grid search and random search techniques.
Hyperparameters are external configuration settings for a machine learning model that are not learned from the data but are set before the training process begins. They significantly influence the performance of the model and are crucial in achieving optimal results.
Examples of hyperparameters include learning rates, regularization strength, the number of hidden layers and units in a neural network, and the choice of a specific algorithm or kernel in certain models.
Selecting appropriate hyperparameter values is essential for obtaining a model that generalizes well to unseen data. Poor choices of hyperparameters can lead to overfitting or underfitting, negatively impacting the model's performance.
- grid_search.ipynb: Jupyter Notebook containing the code for hyperparameter optimization using grid search.
- random_search.ipynb: Jupyter Notebook containing the code for hyperparameter optimization using random search.
- train.csv: CSV file containing the training data used in the machine learning model.
- README.md: This file, providing an overview of the project.
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Clone the repository to your local machine:
git clone https://github.com/your-username/hyperparameter-optimization.git