小编jes*_*lfr的帖子

我一直在尝试计算 Python 中 H2O 模块中梯度增强分类器的 SHAP 值。下面是该predict_contibutions方法的文档中的改编示例（改编自https://github.com/h2oai/h2o-3/blob/master/h2o-py/demos/predict_contributionsShap.ipynb）。

import h2o
import shap
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o import H2OFrame

# initialize H2O
h2o.init()

# load JS visualization code to notebook
shap.initjs()

# Import the prostate dataset
h2o_df = h2o.import_file("https://raw.github.com/h2oai/h2o/master/smalldata/logreg/prostate.csv")

# Split the data into Train/Test/Validation with Train having 70% and test and validation 15% each
train,test,valid = h2o_df.split_frame(ratios=[.7, .15])

# Convert the response column to a factor
h2o_df["CAPSULE"] = h2o_df["CAPSULE"].asfactor()

# Generate a GBM model using …