from sklearn.metrics import accuracy_score from sklearn.model_selection import GridSearchCV from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.externals import joblib # 加载数据 iris = load_iris() data = iris.data target = iris.target # 划...
import joblib # 保存 joblib.dump(lgbm_model, "lgbm_model.pkl") # 加载 my_model = joblib.load("lgbm_model.pkl")
# 1.定义参数config = json.load(open("configs/lightgbm_config.json",'r')) # 2. 构造数据index =int(len(features)*0.9) train_fts, train_lbls = features[:index], labels[:index] val_fts, val_lbls = features[index:], labels[index:] train_data = lgb.Dataset(train_fts, label=train_lb...
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_params()) print(my_best_model.random_seed_) 参考资料 [1] CatBoos...
importlightgbmaslgbfromsklearnimportdatasetsfromsklearn.model_selectionimporttrain_test_splitimportnumpyasnpfromsklearn.metricsimportroc_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_...
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_)) ...
from sklearn.model_selectionimporttrain_test_split from sklearn.externalsimportjoblib # 加载数据 iris=load_iris()data=iris.data target=iris.target # 划分训练数据和测试数据 X_train,X_test,y_train,y_test=train_test_split(data,target,test_size=0.2)# 模型训练 ...
.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() ...
from sklearn.datasets import load_breast_cancer from sklearn.cross_validation import train_test_split canceData=load_breast_cancer() X=canceData.data y=canceData.target X_train,X_test,y_train,y_test=train_test_split(X,y,random_state=0,test_size=0.2) ...
# 导入数据集boston=load_boston()X,y = boston.data,boston.targetX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2,random_state=0) model=LGBMRegressor(boosting_type='gbdt',num_leaves=31,max_depth=-1,learning_rate=0.1,n_estimators=100,objective='regression', # 默认是二...