多变量线性回归中的特征缩放

最新推荐文章于 2024-07-09 13:12:51 发布

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最新推荐文章于 2024-07-09 13:12:51 发布 · 1.5k 阅读

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#特征缩放 #多变量线性回归

为什么要进行特征缩放？

"Most of the times, your dataset will contain features highly varying in magnitudes, units and range. But since, most of the machine learning algorithms use Eucledian distance between two data points in their computations, this is a problem.

If left alone, these algorithms only take in the magnitude of features neglecting the units. The results would vary greatly between different units, 5kg and 5000gms. The features with high magnitudes will weigh in a lot more in the distance calculations than features with low magnitudes.

To supress this effect, we need to bring all features to the same level of magnitudes. This can be acheived by scaling."

以上引自https://medium.com/greyatom/why-how-and-when-to-scale-your-features-4b30ab09db5e

本文以线性回归为例，简单介绍特征缩放在利用梯度下降法求解多变量线性回归方程中参数时的应用：