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Feature importance in XGBoost
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| # credits @mmueller https://www.kaggle.com/mmueller/liberty-mutual-group-property-inspection-prediction/xgb-feature-importance-python/code | |
| import pandas as pd | |
| import xgboost as xgb | |
| import operator | |
| from matplotlib import pylab as plt | |
| def ceate_feature_map(features): | |
| outfile = open('xgb.fmap', 'w') | |
| i = 0 | |
| for feat in features: | |
| outfile.write('{0}\t{1}\tq\n'.format(i, feat)) | |
| i = i + 1 | |
| outfile.close() | |
| features, x_train, y_train = get_data() | |
| ceate_feature_map(features) | |
| importance = gbdt.get_fscore(fmap='xgb.fmap') | |
| importance = sorted(importance.items(), key=operator.itemgetter(1)) | |
| df = pd.DataFrame(importance, columns=['feature', 'fscore']) | |
| df['fscore'] = df['fscore'] / df['fscore'].sum() | |
| plt.figure() | |
| df.plot() | |
| df.plot(kind='barh', x='feature', y='fscore', legend=False, figsize=(6, 10)) | |
| plt.title('XGBoost Feature Importance') | |
| plt.xlabel('relative importance') | |
| plt.gcf().savefig('feature_importance_xgb.png') |
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