Datasets:

tecnologiactc
/

automl_llm_agent_m1

case_id stringclasses 7 values	dataset_name stringclasses 7 values	case_type stringclasses 1 value	input_data_path stringclasses 7 values	processed_reference_path stringclasses 7 values	query_id stringclasses 7 values	user_query stringclasses 7 values	considerations stringclasses 4 values	expected_target stringclasses 7 values	task_family stringclasses 3 values	evaluation_metric stringclasses 3 values	column_name stringlengths 1 49	semantic_type stringclasses 10 values	role stringclasses 4 values	problem_flags stringclasses 11 values	preprocessing_action stringclasses 4 values	outlier_policy stringclasses 2 values	is_target bool 2 classes	is_removed_in_processed bool 2 classes	variant_scope stringclasses 1 value
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		NOPD_Item	unknown	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		Type_	categorical_nominal	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		TypeText	categorical_nominal	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		Priority	encoded_category	feature	encoded_category	cast_to_category	not_applicable	false	false	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		MapX	geolocation	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		MapY	geolocation	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		TimeCreate	timestamp	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		TimeDispatch	timestamp	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		TimeArrive	timestamp	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		TimeClosed	timestamp	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		Disposition	encoded_category	exclude	encoded_category\|removed_in_processed_reference	drop	not_applicable	false	true	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		DispositionText	categorical_nominal	manual_review	target_candidate	manual_review	not_applicable	false	false	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		BLOCK_ADDRESS	categorical_nominal	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		Zip	encoded_category	feature	encoded_category	cast_to_category	not_applicable	false	false	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		PoliceDistrict	encoded_category	feature	encoded_category	cast_to_category	not_applicable	false	false	all
calls_for_service_original	Calls for Service 2013	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Calls_for_Service_2013.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Calls_for_Service_2013_PROCESSED.csv	calls_for_service_original__q001	I need a model that determines whether there will be police action based on the ZIP code, the assigned police district, and the initial priority.		Action	binary_classification		Location	geolocation	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		VIN (1-10)	id	exclude	id_like\|removed_in_processed_reference	drop	not_applicable	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		County	categorical_nominal	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		City	categorical_nominal	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		State	categorical_nominal	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		Postal Code	encoded_category	exclude	encoded_category\|removed_in_processed_reference	drop	not_applicable	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		Model Year	categorical_nominal	feature		keep	not_applicable	false	false	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		Make	categorical_nominal	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		Model	categorical_nominal	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		Electric Vehicle Type	categorical_nominal	feature		keep	not_applicable	false	false	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		Clean Alternative Fuel Vehicle (CAFV) Eligibility	unknown	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		Electric Range	numeric_measurement	feature		keep	manual_review	false	false	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		Base MSRP	target	target		keep	not_applicable	true	false	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		Legislative District	encoded_category	exclude	encoded_category\|removed_in_processed_reference	drop	not_applicable	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		DOL Vehicle ID	id	exclude	id_like\|removed_in_processed_reference	drop	not_applicable	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		Vehicle Location	geolocation	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		Electric Utility	categorical_nominal	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
electric_vehicle_original	Electric Vehicle Population	original	Data/raw/AutoML_LLM_agent/dataset/original_files/Electric_Vehicle_Population_Data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/Electric_Vehicle_Population_Data_PROCESSED.csv	electric_vehicle_original__q001	Given a car's model year, its electric range, and whether it's fully electric or a plug-in hybrid, what should its price be?		Base MSRP	regression		2020 Census Tract	encoded_category	exclude	encoded_category\|removed_in_processed_reference	drop	not_applicable	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		age	numeric_count	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		sex	unknown	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		cp	unknown	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		trestbps	numeric_measurement	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		fbs	unknown	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		restecg	unknown	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		thalach	numeric_measurement	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		exang	unknown	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		oldpeak	numeric_measurement	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		slope	unknown	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		ca	unknown	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		thal	unknown	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		num	unknown	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
cholesterol_original	Cholesterol	original	Data/raw/AutoML_LLM_agent/dataset/original_files/dataset_2190_cholesterol.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/dataset_2190_cholesterol_PROCESSED.csv	cholesterol_original__q001	I want to estimate cholesterol.		chol	regression		chol	numeric_measurement	exclude	removed_in_processed_reference	drop	manual_review	false	true	all
diabetes_original	Diabetes	original	Data/raw/AutoML_LLM_agent/dataset/original_files/diabetes.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/diabetes_PROCESSED.csv	diabetes_original__q001	I want to predict which patients could develop diabetes without any laboratory blood tests.	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	Outcome	binary_classification	recall	Pregnancies	numeric_count	feature		keep	manual_review	false	false	all
diabetes_original	Diabetes	original	Data/raw/AutoML_LLM_agent/dataset/original_files/diabetes.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/diabetes_PROCESSED.csv	diabetes_original__q001	I want to predict which patients could develop diabetes without any laboratory blood tests.	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	Outcome	binary_classification	recall	Glucose	numeric_measurement	exclude	domain_excluded\|removed_in_processed_reference	drop	manual_review	false	true	all
diabetes_original	Diabetes	original	Data/raw/AutoML_LLM_agent/dataset/original_files/diabetes.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/diabetes_PROCESSED.csv	diabetes_original__q001	I want to predict which patients could develop diabetes without any laboratory blood tests.	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	Outcome	binary_classification	recall	BloodPressure	numeric_measurement	feature	implausible_zero	keep	manual_review	false	false	all
diabetes_original	Diabetes	original	Data/raw/AutoML_LLM_agent/dataset/original_files/diabetes.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/diabetes_PROCESSED.csv	diabetes_original__q001	I want to predict which patients could develop diabetes without any laboratory blood tests.	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	Outcome	binary_classification	recall	SkinThickness	numeric_measurement	feature	implausible_zero	keep	manual_review	false	false	all
diabetes_original	Diabetes	original	Data/raw/AutoML_LLM_agent/dataset/original_files/diabetes.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/diabetes_PROCESSED.csv	diabetes_original__q001	I want to predict which patients could develop diabetes without any laboratory blood tests.	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	Outcome	binary_classification	recall	Insulin	numeric_measurement	exclude	domain_excluded\|implausible_zero\|removed_in_processed_reference	drop	manual_review	false	true	all
diabetes_original	Diabetes	original	Data/raw/AutoML_LLM_agent/dataset/original_files/diabetes.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/diabetes_PROCESSED.csv	diabetes_original__q001	I want to predict which patients could develop diabetes without any laboratory blood tests.	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	Outcome	binary_classification	recall	BMI	numeric_measurement	feature	implausible_zero	keep	manual_review	false	false	all
diabetes_original	Diabetes	original	Data/raw/AutoML_LLM_agent/dataset/original_files/diabetes.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/diabetes_PROCESSED.csv	diabetes_original__q001	I want to predict which patients could develop diabetes without any laboratory blood tests.	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	Outcome	binary_classification	recall	DiabetesPedigreeFunction	numeric_measurement	feature		keep	manual_review	false	false	all
diabetes_original	Diabetes	original	Data/raw/AutoML_LLM_agent/dataset/original_files/diabetes.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/diabetes_PROCESSED.csv	diabetes_original__q001	I want to predict which patients could develop diabetes without any laboratory blood tests.	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	Outcome	binary_classification	recall	Age	numeric_count	feature		keep	manual_review	false	false	all
diabetes_original	Diabetes	original	Data/raw/AutoML_LLM_agent/dataset/original_files/diabetes.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/diabetes_PROCESSED.csv	diabetes_original__q001	I want to predict which patients could develop diabetes without any laboratory blood tests.	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	Outcome	binary_classification	recall	Outcome	target	target		keep	not_applicable	true	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	id	id	exclude	id_like\|removed_in_processed_reference	drop	not_applicable	false	true	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	neighborhood	categorical_nominal	exclude	removed_in_processed_reference	drop	not_applicable	false	true	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	latitude	geolocation	feature		keep	manual_review	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	longitude	geolocation	feature		keep	manual_review	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	price	target	target		keep	not_applicable	true	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	size_in_sqft	numeric_measurement	feature		keep	manual_review	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	price_per_sqft	numeric_measurement	exclude	leakage\|removed_in_processed_reference	drop	manual_review	false	true	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	no_of_bedrooms	numeric_count	feature		keep	manual_review	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	no_of_bathrooms	numeric_count	feature		keep	manual_review	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	quality	categorical_nominal	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	maid_room	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	unfurnished	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	balcony	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	barbecue_area	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	built_in_wardrobes	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	central_ac	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	childrens_play_area	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	childrens_pool	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	concierge	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	covered_parking	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	kitchen_appliances	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	lobby_in_building	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	maid_service	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	networked	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	pets_allowed	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	private_garden	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	private_gym	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	private_jacuzzi	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	private_pool	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	security	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	shared_gym	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	shared_pool	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	shared_spa	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	study	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	vastu_compliant	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	view_of_landmark	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	view_of_water	binary_flag	feature		keep	not_applicable	false	false	all
properties_original	Properties	original	Data/raw/AutoML_LLM_agent/dataset/original_files/properties_data.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/properties_data_PROCESSED.csv	properties_original__q001	I want to model real estate prices based on their features	I need a very accurate model, and I don't mind if it's slow, as long as it doesn't take more than 30 minutes. Moreover, I need it to be acurate in high prices	price	regression	RMSE	walk_in_closet	binary_flag	feature		keep	not_applicable	false	false	all
banking_original	Banking	original	Data/raw/AutoML_LLM_agent/dataset/original_files/banking_train_INITIAL.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/banking_train_PROCESSED.csv	banking_original__q001	Which customers are most likely to open a new term deposit given their profile?	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	y	binary_classification		age	numeric_count	feature		keep	manual_review	false	false	all
banking_original	Banking	original	Data/raw/AutoML_LLM_agent/dataset/original_files/banking_train_INITIAL.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/banking_train_PROCESSED.csv	banking_original__q001	Which customers are most likely to open a new term deposit given their profile?	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	y	binary_classification		job	categorical_nominal	feature		keep	not_applicable	false	false	all
banking_original	Banking	original	Data/raw/AutoML_LLM_agent/dataset/original_files/banking_train_INITIAL.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/banking_train_PROCESSED.csv	banking_original__q001	Which customers are most likely to open a new term deposit given their profile?	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	y	binary_classification		marital	categorical_nominal	feature		keep	not_applicable	false	false	all
banking_original	Banking	original	Data/raw/AutoML_LLM_agent/dataset/original_files/banking_train_INITIAL.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/banking_train_PROCESSED.csv	banking_original__q001	Which customers are most likely to open a new term deposit given their profile?	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	y	binary_classification		education	categorical_nominal	feature		keep	not_applicable	false	false	all
banking_original	Banking	original	Data/raw/AutoML_LLM_agent/dataset/original_files/banking_train_INITIAL.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/banking_train_PROCESSED.csv	banking_original__q001	Which customers are most likely to open a new term deposit given their profile?	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	y	binary_classification		default	binary_flag	feature		keep	not_applicable	false	false	all
banking_original	Banking	original	Data/raw/AutoML_LLM_agent/dataset/original_files/banking_train_INITIAL.csv	Data/raw/AutoML_LLM_agent/dataset/processed_files/banking_train_PROCESSED.csv	banking_original__q001	Which customers are most likely to open a new term deposit given their profile?	I want the model to prioritize avoiding false negatives over false positives, since missing patients who might develop diabetes is riskier.	y	binary_classification		balance	numeric_measurement	feature		keep	manual_review	false	false	all

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AutoML-LLM Agent Module 1 Benchmark

This dataset contains the Module 1 benchmark for evaluating an AutoML assistant that interprets user requests, selects tabular modeling settings, produces an auditable AutoGluon Tabular plan, and synthesizes the compact Module 1 recipe consumed by Module 2 through the mandatory final LLM writer used by all A-E variants.

The repository is scoped to Module 1 only.

Tables

cases: one row per Module 1 evaluation case.
queries: one user request per case.
columns_annotations: column-level reference annotations for target, feature, exclusion, leakage, preprocessing, and review decisions.
module1_eval_long: denormalized table for quick inspection in the Hugging Face Dataset Viewer.
dataset_inventory: inventory of the local CSV assets used by the Module 1 cases.
ablation_variants: LangGraph node-ablation variants used by Module 1 experiments. The five benchmark variants are M1_A to M1_E; all end with the final LLM recipe writer.

File Assets

input_data/: raw and processed CSV files referenced by the Module 1 cases.
metadata/M1_reference_output.csv: original reference file used as the starting point.
output_schema.json: public Module 1 output contract, including audit artifacts and the module1_recipe_for_m2 handoff emitted by every benchmark variant.

Current Coverage

The benchmark currently includes seven reference cases:

calls_for_service_original
electric_vehicle_original
cholesterol_original
diabetes_original
properties_original
banking_original
avocado_original

Some cases intentionally document pending or manual-review gaps. In particular, avocado_original is present as a planned case while its CSV files are not yet available locally, and calls_for_service_original is marked for manual review because the requested target differs from the available raw columns.

Usage

from datasets import load_dataset

cases = load_dataset("tecnologiactc/automl_llm_agent_m1", "cases", split="train")
annotations = load_dataset("tecnologiactc/automl_llm_agent_m1", "columns_annotations", split="train")
viewer = load_dataset("tecnologiactc/automl_llm_agent_m1", "module1_eval_long", split="train")

Raw CSV assets referenced by cases.input_data_path can be downloaded from the same dataset repository.

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