This repository demonstrates hyperparameter tuning for email spam classification using GridSearchCV and RandomizedSearchCV with multiple machine learning models in Scikit-Learn.
The aim of this project is to classify emails as spam or not spam using various machine learning models. Hyperparameter tuning is performed to optimize model performance.
- Logistic Regression
- Naive Bayes
- Support Vector Machines (SVM)
- Random Forest
- Gradient Boosting
GridSearchCV is used to perform an exhaustive search over a specified parameter grid. It evaluates all possible combinations of hyperparameters to find the best model.
RandomizedSearchCV performs a random search over specified parameter values. It is more efficient than GridSearchCV for large datasets or when there are many hyperparameters to tune.
The dataset used for this project consists of labeled emails, categorized as spam or not spam.