LGBMClassifier feature ranking

lgbm_feature_ranking.py

imp = pd.DataFrame({'feats':predictors,
                    'ranks': clf.feature_importances_}).sort_values('ranks', ascending=False)

fig, ax = plt.subplots(figsize=(12, 15))

# Example data
features = np.arange(len(imp.feats))
ranking = imp.ranks

ax.barh(features, ranking,  align='center', color='skyblue', ecolor='black')
ax.set_yticks(features)
ax.set_yticklabels(imp.feats)
ax.invert_yaxis()  # labels read top-to-bottom
ax.set_xlabel('Ranking')
ax.set_title('Featre importance')
plt.show()