在Python和R中使用交叉验证方法提高模型性能

内容导读

互联网集市收集整理的这篇技术教程文章主要介绍了在Python和R中使用交叉验证方法提高模型性能，小编现在分享给大家，供广大互联网技能从业者学习和参考。文章包含17373字，纯文字阅读大概需要25分钟。

内容图文

在这里，我们试图找到数量和价格之间的关系。为此，我们采取了以下步骤：

我们使用线性方程式建立了关系，并为其显示曲线图。从训练数据点来看，第一幅图有很高的误差。在这种情况下，我们的模型无法捕获数据的潜在趋势
在第二个图中，我们刚刚发现了价格和数量之间的正确关系，即较低的训练误差
在第三个图中，我们发现训练误差几乎为零的关系。这是因为通过考虑数据点中的每个偏差（包括噪声）来建立关系，即模型过于敏感并且捕获仅在当前数据集中存在的随机模式。这是“过度拟合”的一个例子。
数据科学竞赛的一种常见做法是迭代各种模型以找到性能更好的模型。为了找到正确的答案，我们使用验证技术。

什么是交叉验证？
在给定的建模样本中，拿出大部分样本进行建模型，留小部分样本用刚建立的模型进行预测，并求这小部分样本的预测误差，记录它们的平方和。

以下是交叉验证中涉及的步骤：

保留 样本数据集
使用数据集的其余部分训练模型
使用测试（验证）集的备用样本。帮助您评估模型性能的有效性。
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