one-model和two-model都是先计算输出Y，然后间接求得uplift。model uplift directly 方法希望直接估计uplif...
1. 根据 Uplift Model 计算对照组和实验组中所有样本的 uplift( u(x) = P(x, T=1) - P(x, T=0))2. 根据 u(x) 对对照组和实验组中的样本进行从高到低的降序排序3. 将排序完的样本等频切分成10个bin,找到切分边界值 {b_0,b_1,...,b_K, K=10} 4.依次计算Top k 个bin的平均 Cumulative...
而在uplift tree model中,其本质也还是想要通过衡量分裂前后的变量差值去决策是否分裂节点,不过这里的这个决策差值的计算方法不再是信息增益(information gain),而是不同的直接对增量uplift建模的计算方法,其中包括了利用分布散度对uplift建模和直接对uplift建模。 下面介绍三个Tree-Based算法,Uplift-Tree,CausalForest,CTS。
('~/Github/mr_upliftR/models/mr_uplift_gotv_mult_tmt2') objective_weights = np.concatenate([np.array([(x) for x in range(30)]).reshape(-1,1), -np.ones(30).reshape(-1,1)], axis = 1) erupt_curves_optim, dists_optim = uplift_model_optim.get_erupt_curves(obj...
Model tests on uplift capacity of double-belled pile influenced by distance between bellsJournal of Central South University - To optimize the distance between the bells in pile design, this paper reports a series of small scale tests on the uplift capacity of double belled piles......
3. np.array(['treaet_A' if x==1 else 'cotol' for x in trtent]) # 处理/控制名称 4. 5. 6. 7. RnFostRgesor() # 为model_tau_feature指定模 8. 9. # 在基础学习器中使用feature_importances_方法 10. plot_ipornce() 11. ...
array(['treatment_A' if x==1 else 'control' for x in treatment]) # customize treatment/control names slearner = BaseSRegressor(LGBMRegressor(), control_name='control') slearner.estimate_ate(X, w_multi, y) slearner_tau = slearner.fit_predict(X, w_multi, y) model_tau_feature = ...
# 加载合成数据np.array(['treaet_A'ifx==1else'cotol'forxintrtent])# 处理/控制名称RnFostRgesor()# 为model_tau_feature指定模#在基础学习器中使用feature_importances_方法plot_ipornce()#绘制shap值pot_shp_ues()# interaction_idx设置为'auto'ploshp_dpedece() ...
Python在Scikit-Learn可视化随机森林中的决策树分析房价数据 下一篇 » R语言中的SOM(自组织映射神经网络)对NBA球员聚类分析 引用和评论 注册登录 获取验证码 新手机号将自动注册 登录 微信登录免密码登录密码登录 继续即代表同意《服务协议》和《隐私政策》...
To extend the MLflow logging capabilities, autologging automatically captures the values of input parameters and output metrics of a machine learning model during its training. This information is then logged to the workspace, where the MLflow APIs or the corresponding experiment in the workspace can...