Joint Probability Matrices - Regression Analysis

joint_probability_matricies_3.py

# Regression Analysis

fig, ax = plt.subplots(2, 1, figsize=(20,20))
sns.set(color_codes=True)

print("mean_squared_error: ", round(mean_squared_error(grades_GT, grades_P), 2))
print("mean_absolute_error: ", round(mean_absolute_error(grades_GT, grades_P), 2))
print("explained_variance_score: ", round(explained_variance_score(grades_GT, grades_P), 2))

ax[0].tick_params(axis='both', labelsize=25)
ax[0].set_title("Ground Truth & Predicted Grades - PDF")
ax[0].title.set_size(30)
ax[0].set_xlabel("Grade", fontsize=25)
ax[0].set_ylabel("Density Probability", fontsize=25)
sns.distplot(grades_GT, kde=True, rug=False, ax=ax[0])
sns.distplot(grades_P, kde=True, rug=False, ax=ax[0])

ax[1].tick_params(axis='both', labelsize=25)
ax[1].set_title("Ground Truth & Predicted Grades - Histogram")
ax[1].title.set_size(30)
ax[1].set_xlabel("Grade", fontsize=25)
ax[1].set_ylabel("Frequency", fontsize=25)
sns.distplot(grades_GT, kde=False, rug=True, ax=ax[1])
sns.distplot(grades_P, kde=False, rug=True, ax=ax[1])