I often have two numpy 1d arrays, x and y, and would like to perform some quick sklearn fitting + prediction using them.
import numpy as np
from sklearn import linear_model
# This is an example for the 1d aspect - it's obtained from something else.
x = np.array([1, 3, 2, ...])
y = np.array([12, 32, 4, ...])
Now I'd like to do something like
linear_model.LinearRegression().fit(x, y)...
The problem is that it expects an X which is a 2d column array. For this reason, I usually feed it
x.reshape((len(x), 1))
which I find cumbersome and hard to read.
Is there some shorter way to transform a 1d array to a 2d column array (or, alternatively, get sklearn to accept 1d arrays)?