fromlightgbmimportLGBMClassifierfromsklearn.metricsimportaccuracy_scorefromsklearn.model_selectionimportGridSearchCVfromsklearn.datasetsimportload_irisfromsklearn.model_selectionimporttrain_test_splitfromsklearn.externalsimportjoblib # 加载数据iris = load_iris()data = iris.datatarget...
(1)基于LightGBM原生接口的分类 import lightgbm as lgbfrom sklearn import datasetsfrom sklearn.model_selection import train_test_splitimport numpy as npfrom sklearn.metrics import roc_auc_score, accuracy_score # 加载数据iris = datasets.load_iris() # 划分训练集和测试集X_train, X_test, y_train...
yprob = model.predict_proba(df_cv_train[self.feature_list])[:, 1] _, train_auc, _ = eval_auc(df_cv_train[self.label], yprob) _, train_ks, _ = eval_ks(df_cv_train[self.label], yprob) yprob = model.predict_proba(df_cv_valid[self.feature_list])[:, 1] _, valid_auc, ...
# 保存 joblib.dump(lgbm_model, "lgbm_model.pkl") # 加载 my_model = joblib.load("lgbm_model.pkl")
joblib.dump(gbm,'loan_model.pkl')# 模型加载 gbm=joblib.load('loan_model.pkl')# 模型预测 y_pred=gbm.predict(X_test,num_iteration=gbm.best_iteration_)# 模型评估print('The rmse of prediction is:',mean_squared_error(y_test,y_pred)**0.5)# 特征重要度print('Feature importances:',list(gb...
gbm = joblib.load('loan_model.pkl') # 模型预测 y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration_) # 模型评估 print('The accuracy of prediction is:', accuracy_score(y_test, y_pred)) # 特征重要度 print('Feature importances:', list(gbm.feature_importances_)) ...
model_selection import train_test_split from sklearn.preprocessing import OneHotEncoder import lightgbm as lgb import numpy as np # 以分隔符,读取文件,得到的是一个二维列表 iris = np.loadtxt('iris.data', dtype=str, delimiter=',', unpack=False, encoding='utf-8') # 前4列是特征 data = ...
init_model keep_training_booster 详细方法见增量学习/训练 原理 在LightGBM,Xgboost一直是kaggle的屠榜神器之一,但是,一切都在进步~ 回顾Xgboost 贪心算法生成树,时间复杂度O(ndKlogn)O(ndKlogn),dd个特征,每个特征排序需要O(nlogn)O(nlogn),树深度为KK ...
from sklearn.model_selection import train_test_split import numpy as np from sklearn.metrics import roc_auc_score, accuracy_score # 加载数据 iris = datasets.load_iris() # 划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3)...
.load(args(0)) .repartition(500) val array: Array[Dataset[Row]] = data.randomSplit(Array(0.7, 0.3)) val train_data: Dataset[Row] = array(0) val test_data: Dataset[Row] = array(1) // val lgbCl: LightGBMClassifier = new LightGBMClassifier() ...