core/src/autogluon/core/utils/utils.py
def default_holdout_frac(num_train_rows, hyperparameter_tune=False):
    """ Returns default holdout_frac used in fit().
        Between row count 5,000 and 25,000 keep 0.1 holdout_frac, as we want to grow validation set to a stable 2500 examples.
    """
    if num_train_rows < 5000:
        holdout_frac = max(0.1, min(0.2, 500.0 / num_train_rows))
    else:
        holdout_frac = max(0.01, min(0.1, 2500.0 / num_train_rows))

    if hyperparameter_tune:
        holdout_frac = min(0.2, holdout_frac * 2)  # We want to allocate more validation data for HPO to avoid overfitting

    return holdout_frac
 Returns default holdout_frac used in fit().
        Between row count 5,000 and 25,000 keep 0.1 holdout_frac, as we want to grow validation set to a stable 2500 examples.
    
Returns default holdout_frac used in fit().
Between row count 5,000 and 25,000 keep 0.1 holdout_frac, as we want to grow validation set to a stable 2500 examples.
Returns default holdout_frac used in fit () . Between row count 5 000 and 25 000 keep 0 . 1 holdout_frac as we want to grow validation set to a stable 2500 examples .
def default_holdout_frac(num_train_rows, hyperparameter_tune=False):
    if num_train_rows < 5000:
        holdout_frac = max(0.1, min(0.2, 500.0 / num_train_rows))
    else:
        holdout_frac = max(0.01, min(0.1, 2500.0 / num_train_rows))
    if hyperparameter_tune:
        holdout_frac = min(0.2, holdout_frac * 2)  
    return holdout_frac
def default_holdout_frac ( num_train_rows , hyperparameter_tune = False ) : if num_train_rows < 5000 : holdout_frac = max ( 0.1 , min ( 0.2 , 500.0 / num_train_rows ) ) else : holdout_frac = max ( 0.01 , min ( 0.1 , 2500.0 / num_train_rows ) ) if hyperparameter_tune : holdout_frac = min ( 0.2 , holdout_frac * 2 ) return holdout_frac
Returns default holdout_frac used in fit().
Returns default holdout_frac used in fit () .
""" Returns default holdout_frac used in fit().
        Between row count 5,000 and 25,000 keep 0.1 holdout_frac, as we want to grow validation set to a stable 2500 examples.
    """ # We want to allocate more validation data for HPO to avoid overfitting
num_train_rows
hyperparameter_tune