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=...
# coding:utf-8# pylint:disable=invalid-name,C0111# 函数的更多使用方法参见LightGBM官方文档:http://lightgbm.readthedocs.io/en/latest/Python-Intro.htmlimportjsonimportlightgbmaslgbimportpandasaspd from sklearn.metricsimportmean_squared_error from sklearn.datasetsimportload_iris from sklearn.model_selection...
model_dir ="data/gbm.model" print("model_dir: %s"%model_dir) gbm.save_model("data/gbm.model") printlog("task end...") ### ## # - EOF - 点击标题可跳转 1、 取 27 门语言之长,提升 Python 的能力 2、 总结 | 提高 Python 代码质量的 7 个习惯 3、 一文详解 RNN 股票预测实战(Pyth...
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 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LGBMClassifier类创建模型。你可以设置参数来调整模型的性能。以下是...
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...
Python版本的LightGBM算法原生支持三种并行模式,分别是:特征并行(Featrue Parallelization)和数据并行(Data Parallelization)以及基于投票的数据并行(Voting Parallelization)。而Spark版本的LightGBM不支持特征并行(笔者认为,特征并行不能很好的解决大数据集上的分布式并行处理,因此Spark取消了这种模式),仅支持数据并行和基于投票的...
iris = load_iris() X = iris.data y = iris.target 接着,将数据集拆分为训练集和测试集: from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) ...
model_selection import train_test_split # 加载数据 data = load_breast_cancer() X = data.data y = data.target # 划分数据集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # 将数据转换为LightGBM的Dataset格式 train_data = lgb.Dataset(X...
# 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)) ...