# 可视化第一个prediction的解释 如果不想用JS,传入matplotlib=True shap.initjs() shap.force_plot(explainer.expected_value, shap_values[j], x_new[j]) ''' shap.force_plot(explainer.expected_value, shap_values[j], x_new[j],matplotlib=True) shap.summary_plot(shap_values, df) shap.summary_p...
xgbshapThis is a Go package for calculating feature contributions for XGBoost models. The code is ported from the XGBoost C++ code. This package exists to make it possible to calculate contributions with pure Go code rather than having to use cgo or another language.Missing...
假设几个变量的iv都高，但是相关性也高，模型选的话，只选择其中一个就够了
import xgboost.sklearn as xgb import pandas as pd from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import load_breast_cancer from sklearn.model_selection import train_test_split import shap bc = load_breast_cancer() cancer_df = pd.DataFrame(bc['data'...
Lane change decision prediction: an efficient BO-XGB modelling approach with SHAP analysis zhiyuanl@seu.edu.cnZhiyuan LiuYongqi HuangPu Wanghttps://orcid.org/0000-0001-8772-7584Qixiu ChengHaobo Sun
IV是informative value价值量xgb或light gbm的feature importance是特征重要性，两者计算方式完全不同。大...