save_model('catboost_model.bin') my_best_model.save_model('catboost_model.json', format='json') 当然,导入模型也是非常方便,直接使用load_model 方法 my_best_model.load_model('catboost_model.bin') print(my_best_model.get_par
# 保存 joblib.dump(lgbm_model, "lgbm_model.pkl") # 加载 my_model = joblib.load("lgbm_model.pkl")
importlightgbmaslgbfromsklearn.datasetsimportload_iris# 加载数据集iris = load_iris() X, y = iris.data, iris.target# 定义数据集train_data = lgb.Dataset(X, label=y)# 定义参数params = {'objective':'kmeans','num_leaves':10,'metric':'kmeans', }# 训练模型num_round =100lgb_model = lg...
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...
saveModel.close()#调用模型importpickle folderOfData="C:/Users/my/Desktop/模型/"modelFile=open(folderOfData+'bestGb.pkl','rb') gb=pickle.load(modelFile) modelFile.close()#测试数据#概率转分数defProb2Score(prob, basePoint, PDO):#将概率转化成分数且为正整数y = np.log(prob/(1-prob))retur...
import lightgbm as lgb from sklearn import datasets 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_...
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 = ...
importjoblib# 保存模型joblib.dump(model,"lightgbm_model.pkl")# 加载模型进行预测loaded_model=joblib.load("lightgbm_model.pkl") 1. 2. 3. 4. 5. 6. 7. 结论 通过以上步骤,我们成功地使用 PySpark 部署了 LightGBM,实现了大规模数据的训练和预测。使用这一方案,企业可以在面对海量数据时保持高效,并能够...
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_)) ...
# 模型存储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 accuracy of prediction is:', accuracy_score(y_test, y_pred)) ...