stable_cart.plot_mape_by_prediction¶
- stable_cart.plot_mape_by_prediction(result, ax=None, n_bins=20, class_label=None)[source]¶
Show instability as a function of predicted value: who the model is unsure about.
Averaged over everyone, instability is a single number that hides its own distribution. Binned against the original prediction it answers the question a user actually has — whether the movement is spread evenly or concentrated in the range where decisions get made.
Parameters¶
- result
Output of
bootstrap_predictions().- ax
Axes to draw on. A new figure is created when omitted.
- n_bins
Number of equal-count bins along the predicted-value axis. Equal-count rather than equal-width, so a sparse tail cannot produce a bin of two points and a dramatic-looking mean. A tree predicts one value per leaf, so when there are fewer distinct predictions than bins the distinct values are used directly — otherwise one leaf is split across two bins and the difference between them is noise drawn as signal.
- class_label
Class whose original-fit probability defines the horizontal axis when
resultcontains probability vectors. Required for probability audits. The vertical statistic still measures the full vector.
Returns¶
- Any
The axes.
Raises¶
- ValueError
If
n_binsis below 2, or probability vectors are supplied without one validclass_label.