I have a case with binary results and 1-D features, like: X = np.array(-3,-2,-1,0,1,2,3,) and 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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