lgb_model = lgb.LGBMRegressor( num_leaves=256, reg_alpha=0., reg_lambda=0.01, objective='mae', max_depth=-1, learning_rate=0.03,min_child_samples=25, n_estimators=1200, subsample=0.7, colsample_bytree=0.45,random_state=seed) model=lgb_model.fit(train_x, train['imp']) test_preds+=...
既然问题出在分数偏高,最直接的办法就是把分数偏高的样本找出来,用SHAP计算特征贡献度,看看哪些特征导致分数偏高,代码如下。 importshapexplainer=shap.TreeExplainer(lgb_model)# load modelshap_values=explainer.shap_values(x_test)# x_test是高分样本集合shap_values=shap_values[1]expected_value=explainer.expected...
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) ...
import lightgbm as lgb from sklearn.datasets import load_breast_cancer#加载经典的乳腺癌数据 from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score import matplotlib.pyplot as plt #加载数据集 breast=load_breast_cancer() #获取特征值和目标值 X,y=breast.data...
iris = load_iris() DecisionTree_plot(iris.data,iris.target,iris.feature_names,iris.target_names) 可以看到,使用原生方法可视化的结果还是比较简陋的 2、 决策树可视化方法2,需要安装graphviz软件包 importgraphvizdefDecisionTree_plot2(x,y,feature_names=None,target_names=None,max_depth=3,min_samples_leaf...
Will this mount work for the 86’’ QNED80T model? 1 answer Answer this Question LG Digital Care · 2 months ago Hello.The Slim Wall Mount for LG QNED TVs, Model # WB21LGB can be used with the LG 86-inch Class QNED80T Series 4K QNED TV with webOS 24, Model # 86QNED80TUC...
要将LightGBM的lgb.LGBMClassifier模型权重转换为ONNX格式,你可以按照以下步骤进行操作: 1. 训练LightGBM的lgb.LGBMClassifier模型,并保存模型权重 首先,你需要训练一个LightGBM模型并保存其权重。这里是一个简单的例子: python import lightgbm as lgb from sklearn.datasets import load_iris from sklearn.model_selecti...
importnumpyasnpimportpandasaspdimportxgboostasxgbimporttimefromsklearn.model_selectionimportStratifiedKFoldfromsklearn.model_selectionimporttrain_test_split train_x, train_y, test_x = load_data()# 构建特征# 用sklearn.cross_validation进行训练数据集划分,这里训练集和交叉验证集比例为7:3,可以自己根据需要...
("---模型保存---")withopen(r'./model_v1.pkl','wb')asfile:pickle.dump(gbm,file)print("---模型预测---")withopen(r'./model_v1.pkl','rb')asfile:model=pickle.load(file)y_pred=model.predict(X_test)'''阈值调整'''y_pred=[1ifx>=0.7else0forxiny_pred]print("---指标输出---...
from sklearn.model_selection import train_test_split import lightgbm as lgb from sklearn.ensemble import RandomForestClassifier iris = load_iris() X = iris.data y = iris.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1996) ...