Polynomial Regression

最新推荐文章于 2025-03-08 20:54:30 发布

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本文探讨了在预测房价的应用场景下如何通过特征组合及多项式回归改进模型表现。介绍了将房屋临街宽度与深度结合成单一特征的方法，并讨论了在不同阶数多项式回归中特征缩放的重要性。

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choosing your features
这里写图片描述
defining new features you might actually get a better model
1. combine multiple features into one.
to predict the price of a house
there’s two features:
the frontage of house x1
the depth of the house x2
however we can use X=x1*x2 as the only feature

2.Polynomial Regression
we can fit a quadratic model for it
however this function comes back down against the fact (housing prices won’t go down when the size goes up too high).
then we might choose a cubic function
to realize it
compared with multivariant linear regression we find similarity.(back to the picture)
so choosing three features this way
by the way feature scaling becomes increasingly important
eg. if $x_1$ has range 1 - 1000 then range of $x_1^2$ becomes 1 - 1000000 and that of $x_1^3$ becomes 1 - 1000000000