create_roc_curve

create_roc_curve(
    probs,
    reals,
    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 Receiver Operating Characteristic (ROC) curve.

This function generates an ROC curve, which visualizes the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The curve plots the True Positive Rate (TPR) against the False Positive Rate (FPR) at various threshold settings.

It first calculates the performance data using the provided probabilities and true labels, and then generates the plot.

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). Can be a single array for all probabilities or a dictionary mapping names to label arrays.	required
by	float	The step size for the probability thresholds, controlling the curve’s granularity. 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. A default palette is used if not provided.	['#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 ROC curve.