机器学习算法data 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。

python机器学习实现oneR算法以鸢尾data为例

oneR即“一条规则”。oneR算法根据已有的数据中，具有相同特征值的个体最可能属于哪个类别来进行分类。以鸢尾data为例，该算法实现过程可解读为以下六步：文章目录一、导包与获取数据二、划分为训练集和测试集三、定义函数：获取某特征值出现次数最多的类别及错误率四、定义函数：获取每个特征值下出现...

相册服务中的故事生成算法介绍

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ML之回归预测：利用两种机器学习算法(LiR，XGBoost(调优+重要性可视化+特征选择模型))对无人驾驶汽车系统参数(2017年的data,18+2)进行回归预测值VS真实值

输出结果1、LiR模型LiR：The value of default measurement of LiR is 0.8729775261968014LiR：R-squared value of DecisionTreeRegressor: 0.87297752619680142、XGBoost模...

ML之回归预测：利用十(xgboost,10-1)种机器学习算法对无人驾驶汽车系统参数(2017年的data,18+2)进行回归预测值VS真实值——bug调试记录

输出结果1、增加XGBR算法1、增加XGBR算法时候，采用网格搜索的方法XGBR_grid_model Training time： 135.60037931849538输出XGBR_grid_model模型的最优参数： {'learning_rate': 0.03, 'max_depth': 4,...

ML之回归预测：利用八(9-1)种机器学习算法对无人驾驶汽车参数(2017年的data,18+2)进行回归预测值VS真实值（二）

1.2、模型性能评估及输出预测值各个模型结果LiR LiR：The value of default measurement of LiR is 0.4125342966025278LiR：R-squared value of DecisionTreeRegressor: 0.41253429660...

ML之回归预测：利用八(9-1)种机器学习算法对无人驾驶汽车参数(2017年的data,18+2)进行回归预测值VS真实值（一）

模型评估相关文章：ML之回归预测：利用八(9-1)种机器学习算法对无人驾驶汽车系统参数(2017年的data,18+2)进行回归预测+评估八种模型性能https://blog.csdn.net/qq_41185868/article/details/90383516输出结果1、13.0环境下1.1、...

ML之回归预测：利用八(9-1)种机器学习算法对无人驾驶汽车参数(2017年的data,18+2)进行回归预测+评估八种模型性能

说明在    ML之回归预测：利用九大类机器学习算法对无人驾驶汽车系统参数(18+2)进行回归预测值VS真实值基础上出现了两个bug，成功解决。(1)、成功解决TypeError: unhashable type: 'numpy.ndarray'(2)、成功解决TypeError...

ML之回归预测：利用九大类机器学习算法对无人驾驶汽车系统参数(2018年的data,18+2)进行回归预测值VS真实值

输出结果数据的初步查验：输出回归目标值的差异The max target value is PeakNonedb 89dtype: int64The min target value is PeakNonedb 56dtype: int64The ...

ML之回归预测：利用九大类机器学习算法对无人驾驶系统参数(2018年的data,18+2)进行回归预测+评估九种模型性能

输出记录1、第一次输出错误记录数据的初步查验：输出回归目标值的差异The max target value is PeakNonedb 89dtype: int64The min target value is PeakNonedb 56dtype...

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