[scikit-learn] Question RE: skLearn Logistic Regression

serafim loukas seralouk at hotmail.com
Sat Oct 31 13:21:08 EDT 2020

These are not numpy arrays.


X = np.array([-3,-2,-1,0,1,2,3]).reshape(-1,1)


y = np.array([0, 0, 0, 1, 1, 1, 1]).reshape(-1,1)


On 31 Oct 2020, at 17:51, The Helmbolds via scikit-learn <scikit-learn at python.org<mailto:scikit-learn at python.org>> wrote:

I have a case with binary results and 1-D features, like:

    X = np.array(-3,-2,-1,0,1,2,3,)


    y = np.array(0, 0, 0, 1, 1, 1, 1)

only longer arrays (about 180 entries in each array) of this general type.

So this should be the "simplest" case.

Altho I've tried several variations of the Logistic input formats, in

   LogisticRegression.fit(X, y)

they keep being rejected with the most common error message being

   Missing argument y

I assure you I do indeed have an array "y" that is passed to "fit"

So----What do I have to do to get Logistic Regression to accept 1-D features?

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