[FIXED] How to fit a polynomial curve to data using scikit-learn?

Issue

Problem context

Using scikit-learn with Python, I'm trying to fit a quadratic polynomial curve to a set of data, so that the model would be of the form y = a2x^2 + a1x + a0 and the an coefficients will be provided by a model.

The problem

I don't know how to fit a polynomial curve using that package and there seem to be surprisingly few, clear references on how to do it (I've looked for a while). I've seen this question on doing something similar with NumPy, and also this question which does a more complicated fit than I require.

What a good solution would look like

Hopefully, a good solution would go around like this (sample adapted from linear fit code that I'm using):

x = my_x_data.reshape(len(profile), 1)
y = my_y_data.reshape(len(profile), 1)
regression = linear_model.LinearRegression(degree=2) # or PolynomialRegression(degree=2) or QuadraticRegression()
regression.fit(x, y)

I would imagine scikit-learn would have a facility like this, since it's pretty common (for example, in R, the formula for fitting can be provided in-code, and they should be able to be pretty interchangeable for that kind of use-case).

The question:

What is a good way to do this, or where can I find information about how to do this properly?

Solution

Possible duplicate: https://stats.stackexchange.com/questions/58739/polynomial-regression-using-scikit-learn.

Is it crucial for some reason that this be done using scikit-learn? The operation you want can be performed very easily using numpy:

z = np.poly1d(np.polyfit(x,y,2))

After which z(x) returns the value of the fit at x.

A scikit-learn solution would almost certainly be simply a wrapper around the same code.

Answered By - AGML