create_decision_curve

create_decision_curve(
    probs,
    reals,
    decision_type='conventional',
    min_p_threshold=0,
    max_p_threshold=1,
    by=0.01,
    stratified_by=['probability_threshold'],
    size=600,
    color_values=['#1b9e77', '#d95f02', '#7570b3', '#e7298a', '#07004D', '#E6AB02', '#FE5F55', '#54494B', '#006E90', '#BC96E6', '#52050A', '#1F271B', '#BE7C4D', '#63768D', '#08A045', '#320A28', '#82FF9E', '#2176FF', '#D1603D', '#585123'],
)

Creates a Decision Curve.

Decision Curve Analysis is a method for evaluating and comparing prediction models that incorporates the clinical consequences of a decision. The curve plots the net benefit of a model against the probability threshold used to determine positive cases. This helps to assess the real-world utility of a model.

Parameters

Name	Type	Description	Default
probs	Dict[str, np.ndarray]	A dictionary mapping model or dataset names to 1-D numpy arrays of predicted probabilities.	required
reals	Union[np.ndarray, Dict[str, np.ndarray]]	The true binary labels (0 or 1).	required
decision_type	str	Type of decision curve. "conventional" for a standard decision curve or another value for the “interventions avoided” variant. Defaults to "conventional".	'conventional'
min_p_threshold	float	The minimum probability threshold to plot. Defaults to 0.	0
max_p_threshold	float	The maximum probability threshold to plot. Defaults to 1.	1
by	float	The step size for the probability thresholds. Defaults to 0.01.	0.01
stratified_by	Sequence[str]	Variables for stratification. Defaults to ["probability_threshold"].	['probability_threshold']
size	int	The width and height of the plot in pixels. Defaults to 600.	600
color_values	List[str]	A list of hex color strings for the plot lines.	['#1b9e77', '#d95f02', '#7570b3', '#e7298a', '#07004D', '#E6AB02', '#FE5F55', '#54494B', '#006E90', '#BC96E6', '#52050A', '#1F271B', '#BE7C4D', '#63768D', '#08A045', '#320A28', '#82FF9E', '#2176FF', '#D1603D', '#585123']

Returns

Name	Type	Description
	Figure	A Plotly Figure object representing the Decision Curve.