Model Hyperparameter Tuning Techniques Comparison

This project compares various hyperparameter tuning techniques for optimizing machine learning models. It includes implementations of different tuning methods using popular libraries such as scikit-learn, scikit-optimize (skopt), hyperopt, Optuna, and manual grid search/random search.

Techniques Implemented

1. Bayesian Optimization with skopt (bayesian_search.py):

   • Utilizes Gaussian process-based Bayesian optimization for hyperparameter tuning.
   • Defines a custom optimization function using gp_minimize from skopt.
   • Searches the parameter space for RandomForestClassifier hyperparameters.
   • Uses 5-fold cross-validation for evaluation.

2. Custom Scoring with scikit-learn (custom_scoring.py):

   • Implements hyperparameter tuning using scikit-learn's RandomizedSearchCV.
   • Defines a custom parameter grid and scoring metric (accuracy) for RandomForestClassifier.
   • Performs randomized search over the parameter grid using cross-validation.

3. Grid Search with scikit-learn (grid_search.py):

   • Implements hyperparameter tuning using scikit-learn's GridSearchCV.
   • Searches through a predefined grid of hyperparameters for RandomForestClassifier.
   • Evaluates model performance using 5-fold cross-validation.

4. Hyperopt Optimization (hyperopt_search.py):

   • Utilizes tree-structured Parzen Estimator (TPE) algorithm for hyperparameter optimization.
   • Defines a search space and optimization function using Hyperopt library.
   • Searches for optimal hyperparameters for RandomForestClassifier using 5-fold cross-validation.

5. Optuna Optimization (optuna_search.py):

   • Implements hyperparameter optimization using Optuna library.
   • Defines an objective function and search space for RandomForestClassifier hyperparameters.
   • Searches for optimal hyperparameters using Bayesian optimization with TPE sampler.

6. Random Search with scikit-learn (random_search.py):

   • Performs hyperparameter tuning using scikit-learn's RandomizedSearchCV.
   • Searches randomly across the parameter space for RandomForestClassifier.
   • Utilizes 5-fold cross-validation for evaluation.

Dataset

• The project utilizes the train.csv dataset for training machine learning models.
• The dataset contains features and target variable ('price_range').

Getting Started

1. Clone the repository.
2. Ensure you have the required dependencies installed (pandas, numpy, scikit-learn, scikit-optimize, hyperopt, optuna, dlib).
3. Run each Python script to see the implementation of different hyperparameter tuning techniques.
4. Compare the results obtained by different methods to understand their performance.

Conclusion

• Compare the performance and efficiency of each hyperparameter tuning method based on their results.
• Consider factors such as computational resources, tuning time, and optimization effectiveness when choosing a method for model tuning in practical applications.

🙂 Feel free to contribute, provide feedback, or suggest improvements to the project!