Dataset for Adaptive sampling-based surrogate modeling for composite performance prediction

This dataset contains the raw data for the paper titled "Adaptive sampling-based surrogate modeling for composite performance prediction" and the trained Gausssian process regression (GPR) model from adaptive sampling.
Folder Summary:
The root folder contains a subfolder 'saved_model' and relevant files for training.
-The `saved_model` subfolder contains: Multiple versions of serialized models (`.joblib` and `.pkl`), reflecting variations in model configurations or training epochs.
-'model_20_5_20.pkl' file contains the settings of a saved model with 20 seed data, 5 queries and 20 sampling size in Pickel format
-'model_20_5_20.joblib' file contains the settings of a saved model with 20 seed data, 5 queries and 20 sampling size in Joblib format
- Training notebook (`AL_gp_training.ipynb`) to manage and execute training workflows.
- Dataset (`Data_all.csv`) in CSV format for training and testing purposes.
-Readme ('Readme.md') to run the code