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polynomial feature


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1 برق و الکترونیک:: مشخصه چند جمله ای

For example, the dashed line in the right plot on Figure 4-18 (with α = 10-7) looks quadratic, almost linear: all the weights for the high-degree polynomial features are equal to zero. One approach to handling nonlinear datasets is to add more features, such as polynomial features (as you did in Chapter 4); in some cases this can result in a linearly separable dataset. Linear SVM classifier using polynomial features Adding polynomial features is simple to implement and can work great with all sorts of Machine Learning algorithms (not just SVMs), but at a low polynomial degree it cannot deal with very complex datasets, and with a high polynomial degree it creates a huge number of features, making the model too slow. It makes it possible to get the same result as if you added many polynomial features, even with very high- degree polynomials, without actually having to add them.

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