AUTOXGBOOST + Optuna An auto version of one of the most powerful machine learning XGBOOST Pt.1

AUTOXGBOOST: Optimal Parameters

from autoxgb import AutoXGB

# Define required parameters
train_filename = "credit_data.csv" # Path to training dataset
#train_filename = X_train
output = r"......autoXBG\output_autoxgb" # Name of output folder

# Set optional parameters
# path to test data. if specified, the model will be evaluated on the test data
# and test_predictions.csv will be saved to the output folder
# if not specified, only OOF predictions will be saved
# test_filename = "test.csv"

test_filename = None
task = "classification"
targets = ["default"]
use_gpu = True
num_folds = 5
seed = 42
num_trials = 200
time_limit = 400
fast = False
features = None
categorical_features = None