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DS-1000 / 482 / Suppose I have a hypotetical function I'd like to approximate:

Problem

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Problem:
Suppose I have a hypotetical function I'd like to approximate:
def f(x):
    return a * x ** 2 + b * x + c
Where a, b and c are the values I don't know.
And I have certain points where the function output is known, i.e.
x = [-1, 2, 5, 100]
y = [123, 456, 789, 1255]
(actually there are way more values)
I'd like to get a, b and c while minimizing the squared error .
What is the way to do that in Python? The result should be an array like [a, b, c], from highest order to lowest order.
There should be existing solutions in numpy or anywhere like that.
A:
<code>
import numpy as np
x = [-1, 2, 5, 100]
y = [123, 456, 789, 1255]
</code>
result = ... # put solution in this variable
BEGIN SOLUTION
<code>

library

Numpy

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Source and history

upstream_id / Original problem ID
482
task_id
2741339b-198d-5c63-8825-e24d50d7a30a
task_key
default--test--482
task_revision_id
1
config / split
default / test

Official source

initial import