refine

refine(
    n_points=10,
    max_gap_ratio=MAX_GAP_RATIO,
    min_r2_score=MIN_R2_SCORE,
    max_avg_cv=MAX_AVG_CV,
    max_max_cv=MAX_MAX_CV,
    max_allowed_vif=MAX_ALLOWED_VIF,
    seed=None,
)

Identifies gaps or high-variance regions and suggests new simulation points.

Parameters

Name	Type	Description	Default
n_points	int	Number of new samples to suggest per detected issue.	`10`
max_gap_ratio	float	Largest allowed coverage gap, as a fraction of each input’s range.	`MAX_GAP_RATIO`
min_r2_score	float	Minimum cross-validated R² for the model-fit check.	`MIN_R2_SCORE`
max_avg_cv	float	Maximum average bootstrap spread (relative to the response std).	`MAX_AVG_CV`
max_max_cv	float	Maximum bootstrap spread at any probe point.	`MAX_MAX_CV`
max_allowed_vif	float	Maximum Variance Inflation Factor for the collinearity check.	`MAX_ALLOWED_VIF`
seed	int	Seed for reproducible suggestions.	`None`

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: A DataFrame of recommended new input coordinates.

Examples

# If diagnostics fail, ask for 10 new points to fix it
new_samples = study.refine(n_points=10)
print(new_samples.head())