Prediction Error Bias

import matplotlib.pyplot as plt
import numpy as np

COLORS = {'over': '#EF4444', 'under': '#3B82F6', 'background': '#FFFFFF', 'text': '#1E293B', 'text_muted': '#64748B', 'grid': '#F1F5F9'}

np.random.seed(42)
error = np.random.normal(-0.5, 3, 500)

fig, ax = plt.subplots(figsize=(10, 6), dpi=100)
ax.set_facecolor(COLORS['background'])
fig.patch.set_facecolor(COLORS['background'])

counts, bins, patches = ax.hist(error, bins=35, edgecolor='white', linewidth=0.5)
for i, patch in enumerate(patches):
    bin_center = (bins[i] + bins[i+1]) / 2
    patch.set_facecolor(COLORS['over'] if bin_center > 0 else COLORS['under'])
    patch.set_alpha(0.85)

ax.axvline(0, color=COLORS['text'], linewidth=2, label='Zero Bias')
ax.axvline(np.mean(error), color='#F59E0B', linewidth=2, linestyle='--', label=f'Bias: {np.mean(error):.2f}')

ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['left'].set_color(COLORS['grid'])
ax.spines['bottom'].set_color(COLORS['grid'])
ax.yaxis.grid(True, color=COLORS['grid'], linewidth=1, zorder=0)
ax.set_axisbelow(True)
ax.tick_params(axis='both', colors=COLORS['text_muted'], labelsize=9, length=0, pad=8)
ax.set_xlabel('Prediction Error', fontsize=10, color=COLORS['text'], labelpad=10)
ax.set_ylabel('Samples', fontsize=10, color=COLORS['text'], labelpad=10)
ax.legend(loc='upper center', bbox_to_anchor=(0.5, -0.12), ncol=2, frameon=False, fontsize=9, labelcolor=COLORS['text_muted'])

plt.tight_layout()
plt.show()