jablonkagroup/corral-environment-tasks huggingface dataset:
Task: Train XGBoost model
Environment: ml (level_1 task)
Description: Generate a comprehensive dataset of oxide polymorphs from Materials Project and train an XGBoost model to predict formation energies. Evaluate the trained XGBoost model using test set and cross-validation metrics. Save the results as a JSON file with keys `test_set_evaluation` and `cross_validation_results`. The test_set_evaluation dictionary must contain `mae`, `rmse`, `r2`, and `feature_importance`. The cross_validation_results dictionary must contain `r2_mean` and `r2_std`.
Allowed tools: batch_retrieve_polymorphs, filter_json_with_strategy, select_polymorphs_with_strategy_to_file, consolidate_polymorph_datasets, select_polymorphs_with_strategy, get_bulk_polymorphs_data, sort_and_get_first_from_json, get_bulk_polymorphs_data_to_file, prepare_tabular_dataset, train_xgboost_model, evaluate_xgboost_model, perform_cross_validation
Submission format: path to the results file (eg. model_evaluation_results.json)
Task: Train XGBoost model
Environment: ml (level_1 task)
Description: Generate a comprehensive dataset of nitrides polymorphs from Materials Project and train an XGBoost model to predict formation energies. Evaluate the trained XGBoost model using test set and cross-validation metrics. Save the results as a JSON file with keys `test_set_evaluation` and `cross_validation_results`. The test_set_evaluation dictionary must contain `mae`, `rmse`, `r2`, and `feature_importance`. The cross_validation_results dictionary must contain `r2_mean` and `r2_std`.
Allowed tools: batch_retrieve_polymorphs, filter_json_with_strategy, select_polymorphs_with_strategy_to_file, consolidate_polymorph_datasets, select_polymorphs_with_strategy, get_bulk_polymorphs_data, sort_and_get_first_from_json, get_bulk_polymorphs_data_to_file, prepare_tabular_dataset, train_xgboost_model, evaluate_xgboost_model, perform_cross_validation
Submission format: path to the results file (eg. model_evaluation_results.json)
Task: Train XGBoost model
Environment: ml (level_1 task)
Description: Generate a comprehensive dataset of sulphides polymorphs from Materials Project and train an XGBoost model to predict formation energies. Evaluate the trained XGBoost model using test set and cross-validation metrics. Save the results as a JSON file with keys `test_set_evaluation` and `cross_validation_results`. The test_set_evaluation dictionary must contain `mae`, `rmse`, `r2`, and `feature_importance`. The cross_validation_results dictionary must contain `r2_mean` and `r2_std`.
Allowed tools: batch_retrieve_polymorphs, filter_json_with_strategy, select_polymorphs_with_strategy_to_file, consolidate_polymorph_datasets, select_polymorphs_with_strategy, get_bulk_polymorphs_data, sort_and_get_first_from_json, get_bulk_polymorphs_data_to_file, prepare_tabular_dataset, train_xgboost_model, evaluate_xgboost_model, perform_cross_validation
Submission format: path to the results file (eg. model_evaluation_results.json)