Optuna

Optuna is a Hyperparameter optimization framework used to automatically tune hyperparameters for machine learning models.

Steps to Use Optuna

1. Define Objective Functions:

   • For each model (LightGBM, XGBoost, CatBoost), define an objective function.
   • The objective function takes trial parameters as input and returns a score to optimize.
   • Specify hyperparameters to tune within each function, such as learning rate, number of leaves (for LightGBM), eta, max depth (for XGBoost), and learning rate, depth (for CatBoost).

2. Running Hyperparameter Optimization:

   • Create a study object for each model using optuna.create_study().
   • Run the optimization process using .optimize() method, specifying the objective function and the number of trials.
   • Retrieve the best hyperparameters from each study object using .best_params.

3. Comparison and Evaluation:

   • Compare the best hyperparameters obtained for each model.
   • Evaluate the performance of the tuned models on a validation dataset.

Differences between Models with Optuna

• Hyperparameters:

  • The specific hyperparameters to tune may vary between models.
  • For example, LightGBM may involve tuning parameters like learning rate and number of leaves, while XGBoost may involve parameters like eta and max depth.

• Objective Function:

  • The objective function for each model should be tailored to its respective API and requirements.
  • Ensure that the objective function properly trains and evaluates the model using the specified hyperparameters.

• Optimization Strategy:

  • Optuna provides different optimization algorithms (e.g., TPE, CMA-ES) that may behave differently depending on the model and hyperparameter space.
  • Experiment with different optimization strategies to find the most effective one for each model.