Hello, uncertainty (glimpse).

This tiny example shows how repeated stochastic predictions can be summarized into mean probabilities and visualized as error bars.

from __future__ import annotations

import matplotlib.pyplot as plt
import numpy as np

# Three stochastic passes for one instance and four classes
passes = np.array(
    [
        [0.1, 0.2, 0.6, 0.1],
        [0.2, 0.2, 0.5, 0.1],
        [0.15, 0.25, 0.5, 0.1],
    ]
)

mean = passes.mean(axis=0)
std = passes.std(axis=0, ddof=0)

classes = np.arange(mean.shape[0])
plt.figure(figsize=(4, 2.5))
plt.errorbar(classes, mean, yerr=std, fmt="o", capsize=4)
plt.xticks(classes)
plt.ylim(0, 1)
plt.xlabel("Class index")
plt.ylabel("Probability")
plt.title("Mean ± std over stochastic passes")
plt.tight_layout()