机器学习算法lightgbm 的相关内容

[帮助文档] 机器学习线性支持向量机算法组件的配置及示例

支持向量机SVM（Support Vector Machine）是基于统计学习理论的一种机器学习方法，通过寻求结构风险最小化，提高学习机泛化能力，从而实现经验风险和置信范围最小化。本文介绍线性支持向量机算法组件的配置方法及使用示例。

ML之回归预测：利用十类机器学习算法(线性回归、kNN、SVM、决策树、随机森林、极端随机树、SGD、提升树、LightGBM、XGBoost)对波士顿数据集回归预测(模型评估、推理并导到csv)

目录利用十类机器学习算法(线性回归、kNN、SVM、决策树、随机森林、极端随机树、SGD、提升树、LightGBM、XGBoost)对波士顿数据集【13+1,506】回归预测(模型评估、推理并导到csv)输出数据集1、LiR 线性回归算法2、kNNR k最近邻算法3、SVMR 支持向量机算法4、DT...

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②机器学习分类算法之LightGBM（梯度提升框架）

代码实践参数详解一下参数可以提高准确率learning_rate:学习率.默认值：0.1调参策略：最开始可以设置得大一些，如0.1。调整完其他参数之后最后再将此参数调小。取值范围:0.01~0.3.max_depth:树模型深度默认值：-1调整策略:无取值范围：3-8（不超过10）num_leave...

①机器学习分类算法之LightGBM（梯度提升框架）

走进LightGBM什么是LightGBM？在上一篇的文章里，我介绍了XGBoost算法，它是是很多的比赛的大杀器，但是在使用过程中，其训练耗时很长，内存占用比较大。在2017年年1月微软在GitHub的上开源了LightGBM。该算法在不降低准确率的前提下，速度提升了10倍左右，占用...

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更新时间 2024-02-03 14:17:22

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