Xgbfi
用于训练好的xgboost模型分析对应特征的重要性，当然你也可以使用fmap来观察
What is Xgbfi?
Xgbfi is a XGBoost model dump parser, which ranks features as well as feature interactions by different metrics.
Siblings
Xgbfir – Python porting
衡量准则
- Gain: Total gain of each feature or feature interaction
- FScore: Amount of possible splits taken on a feature or feature interaction
- wFScore: Amount of possible splits taken on a feature or feature interaction weighted by the probability of the splits to take place
- Average wFScore: wFScore divided by FScore
- Average Gain: Gain divided by FScore
- Expected Gain: Total gain of each feature or feature interaction weighted by the probability to gather the gain
- Average Tree Index
- Average Tree Depth
其他功能
- Leaf Statistics
- Split Value Histograms
评判准则的相关说明:
python包安装
Using pip
You can install using the pip package manager by running
pip install xgbfir
From source
Clone the repo and install:
| git clone https://github.com/limexp/xgbfir.git | |
| cd xgbfir | |
| sudo python setup.py install |
Or download the source code by pressing ‘Download ZIP’ on this page. Install by navigating to the proper directory and running
sudo python setup.py install
快速上手
| from sklearn.datasets import load_iris, load_boston | |
| import xgboost as xgb | |
| import xgbfir | |
| # loading database | |
| boston = load_boston() | |
| # doing all the XGBoost magic | |
| xgb_rmodel = xgb.XGBRegressor().fit(boston[‘data’], boston[‘target’]) | |
| # saving to file with proper feature names | |
| xgbfir.saveXgbFI(xgb_rmodel, feature_names=boston.feature_names, OutputXlsxFile=‘bostonFI.xlsx’) | |
| # loading database | |
| iris = load_iris() | |
| # doing all the XGBoost magic | |
| xgb_cmodel = xgb.XGBClassifier().fit(iris[‘data’], iris[‘target’]) | |
| # saving to file with proper feature names | |
| xgbfir.saveXgbFI(xgb_cmodel, feature_names=iris.feature_names, OutputXlsxFile=‘irisFI.xlsx’) |
现在你看下生成的excel文件
参考
https://github.com/limexp/xgbfir
https://github.com/Far0n/xgbfi
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