Recognizing Polynomial Regression

My question is that how we recognize that input data (which has multiple features such as - x1,x2,…xn ) is in polynomial regression since we can’t check it by plotting because it has more than 2 input features?

Why do you need to check it?

Hi there

Data understanding with the right visualization is often very valuable. In this thread a possibility was discussed for data viz which also works for a several features with a standard pandas DF, see also:

At least you see here how features are distributed and whether they are correlated. It is recommended to do this kind of visualization also in the course of your residual analysis in order to see if your model (e.g. your polynomial fit) has systematic flaws and if it generalises well on your test set w/o specific correlations to features (site note: often oscillations at the border can be an issue with polynomial regression models).

@Harshit_Kumar2: please let me know if this helps.

Best regards

Here an exemplary visualization of a model, depicting the dependencies of the 2 most meaningful features:

Here an example for the residual analysis I outlined:

Of course you could also apply this for your polynomial model. Source is linked in previous post.

Best regards