When executing the following code to find the features importance of a LGBMRegressor model I get a KeyError: 'objective' error. The model's objective was specified as 'mean_squared_error'. Code: explainer = shap.TreeExplainer(Final_Model...
称为折叠 (fold) （如果 k = n， 这等价于 Leave One Out（留一） 策略），都具有相同的大小（...
effect_pred =model.estimate_effect(identified_estimand=estimand, method_name='backdoor.econml.metalearners.SLearner', target_units = earnings_interaction_test.drop(['true_effect', 'took_a_course'], axis=1), method_params={ 'init_params': {'overall_model': LGBMRegressor(n_estimators=500, ma...
1.特征重要性分析:检查特征重要性是否在合理范围内,可以使用特征贡献占比(比如LGBM模型可以通过Feature Importance来计算各个特征的information gain来计算占比,或通过特征的sharply value来计算占比)检查是否有比重过大的特征,如果头部的特征贡献比重超过一定阈值(可以根据情况设定)可以判定为有信息泄露的可能。 2.皮尔森...
Precision-recall curve for LGBM algorithm. Full size image Most of the algorithms used in this research are black-box types. Their primary objective is to provide results but no detail on how each variable influences decision-making, leaving aside the interpretability given to academic stakeholders....
This model optimizes the integration of Random Forest (RF), CatBoost (CB), and Light Gradient Boosting Machine (LGBM) using grid search optimization. The Stack-ClimaBoost model outperforms previous state-of-the-art models obtaining a low MAPE 0.765, an RMSE of 2.254, and an ...
LGBMRegressor 12 8 66.67 CatBoostClassifier 10 6 60.0 CatBoostRegressor 12 8 66.67 Installation pip install target-permutation-importances or with poetry: poetry add target-permutation-importances If with Python 3.8.x & Pandas 1.x.x: pip install target-permutation-importances==1.19.0 or with...
from lightgbm import LGBMRegressor import time import os import errno from multiprocessing import cpu_count n_cpus = cpu_count() - 1 def prepare_rolling_train(df,features_column,label_column,date_column,unique_datetime,testing_windows,first_trade_date_index, max_rolling_window_index,current_...
(DRIVER_CORES//parallel_grid_search_jobs, 1), } gbm_regressor = lgb.LGBMRegressor(objective=params['objective'], num_leaves=params['num_leaves'], boosting_type=params['boosting_type'], metric=params['metric'], bagging_fraction=params['bagging_fraction'], bagging_freq=params['bagging_freq'...
et al. A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Fore- casting. Energy 214, 118874 (2021). 17. Rao, K. R., Kim, D. N. & Hwang, J. J. Integer fast fourier transform. In Fast Fourier Transform—Algorithms and Applications 111–126 (...