plot_decision_curve()

Plots a Decision Curve from pre-computed performance data.

Usage

Source

plot_decision_curve(
    performance_data,
    decision_type="conventional",
    min_p_threshold=0,
    max_p_threshold=1,
    stratified_by=["probability_threshold"],
    size=600
)

This function is useful for plotting a Decision Curve directly from a DataFrame that already contains the necessary performance metrics.

Parameters

performance_data: pl.DataFrame

A Polars DataFrame with performance metrics, including net benefit and probability thresholds.

decision_type: str = "conventional"

Type of decision curve to plot. Defaults to "conventional".

min_p_threshold: float = 0

The minimum probability threshold to plot. Defaults to 0.

max_p_threshold: float = 1

The maximum probability threshold to plot. Defaults to 1.

stratified_by: Sequence[str] = ["probability_threshold"]


The columns in performance_data used for stratification. Defaults to ["probability_threshold"].

size: int = 600

The width and height of the plot in pixels. Defaults to 600.

Returns

Figure

A Plotly Figure object representing the Decision Curve.