Always y_test then y_pred / y_probs, known and then unknown…
Model
sklean takes values for most of its model fitting, so use .values which returns the ndarray of the column we are looking at.
Visualize it as \(X = \begin{bmatrix}x_{1} \\ x_{2} \\ x_{3} \\ \end{bmatrix}\) and \(Y = \begin{bmatrix}y_{1} & y_{2} & y_{3}\end{bmatrix}\) where \(x_{i}\) are \(\begin{bmatrix}x_{i1} & x_{i 2} & \dots & x_{i n}\end{bmatrix}\). So a caveat would be if we are using \(x_{1}\) as scalars (with only one dimension). In such a case we will need to pass \(\begin{bmatrix}[x_{1}] \\ [x_{2}] \\ [x_{3}]\end{bmatrix}\) instead of just \(\begin{bmatrix}x_{1} & x_{2} & x_{3}\end{bmatrix}\) thus we need to use the reshape method and reshape it like so X.reshape(-1, 1) to tell pandas that \(X\) should have only 1 column, and -1 tells that any number of observations as required.