# 保存 joblib.dump(lgbm_model, "lgbm_model.pkl") # 加载 my_model = joblib.load("lgbm_model.pkl")
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 # 划分...
from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split 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=42) 创建LGBMClassifier模型使用LightGBM库中的LGBMClassif...
可以使用scikit-learn库中的一个示例数据集来演示,如鸢尾花数据集: fromsklearn.datasetsimportload_iris data=load_iris()X=data.data y=data.target 1. 2. 3. 4. 5. 接下来,我们需要将数据集划分为训练集和测试集: fromsklearn.model_selectionimporttrain_test_split X_train,X_test,y_train,y_test=...
from sklearn.model_selectionimporttrain_test_split from sklearn.datasetsimportmake_classification iris=load_iris()# 载入鸢尾花数据集 data=iris.data target=iris.target X_train,X_test,y_train,y_test=train_test_split(data,target,test_size=0.2)# 加载你的数据 ...
.appName("Python Lightgbm") \ .config('spark.jars.packages', "com.microsoft.ml.spark:mmlspark_2.11:0.18.1") \ .getOrCreate() df_train = spark.read.format("csv") \ .option("inferSchema", "true") \ .option("header", "true") \ ...
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] CatBoost JSON 模型教程: github.com/catboost/tut [2] Python: catboost.ai/en/docs/con [3] C++: catboost....
importlightgbmaslgbfromsklearn.model_selectionimporttrain_test_splitfromsklearn.datasetsimportload_breast_cancer 然后,我们加载数据并划分训练集和测试集: # 加载数据data=load_breast_cancer()X=data.data y=data.target# 划分训练集和测试集X_train,X_test,y_train,y_test=train_test_split(X,y,test_size...
以下是一个简单的Python示例代码,展示了如何使用LightGBM进行二分类任务: # 导入必要的库 import lightgbm as lgb from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score # 加载数据集 X, y = load_your_dataset() # 划分训练集和测试集 X_train, X_test, y_trai...
LightGBM作为常见的强大Python机器学习工具库,安装也比较简单。 这些系统下的 XGBoost 安装,大家只要基于pip就可以轻松完成了,在命令行端输入命令如下命令即可等待安装完成。 pip install lightgbm 大家也可以选择国内的pip源,以获得更好的安装速度: pip install -i https://pypi.tuna.tsinghua.edu.cn/simple lightgbm...