https://github.com/martinkalema/hyperparameter-tuning-gridsearchcv
Using GridSearchCV to tune hyperparameters for my logistic regression model to better model performance
https://github.com/martinkalema/hyperparameter-tuning-gridsearchcv
gridsearchcv logistic-regression machine-learning
Last synced: 7 months ago
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Using GridSearchCV to tune hyperparameters for my logistic regression model to better model performance
- Host: GitHub
- URL: https://github.com/martinkalema/hyperparameter-tuning-gridsearchcv
- Owner: MartinKalema
- License: apache-2.0
- Created: 2024-02-06T17:39:45.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-02-10T15:10:10.000Z (over 1 year ago)
- Last Synced: 2025-01-11T09:16:29.467Z (9 months ago)
- Topics: gridsearchcv, logistic-regression, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 105 KB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Hyperparameter-Tuning-GridSearchCV
Grid search cross-validation (GridSearchCV) is a technique used in machine learning to find the optimal hyperparameters for a model and it involves defining a grid of hyperparameters to search over.
It automates the process of tuning hyperparameters, which can be tedious and time-consuming if done manually and helps in finding the best combination of hyperparameters that yields the best performance for the given dataset and model.
## Dataset Links
https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset
## Clone the Repository
```
git clone https://github.com/MartinKalema/Hyperparameter-Tuning-GridSearchCV
```