I'm trying to implement a logistic regression with Sklearn. Currentely I have a Dataframe which consists of 12 input variables and 1 output variable.
The output dataframe is binary valued whereas the remaining 12 variables are not necessarily so.
Example how the input data is structured.
#PseudoCode (Y and X are pandas dataframes)
Y = 0, 1, 0, 1, 1, 1 # Output data
X = A1: 1, 1, 2, 1, 2, 2 #Input Data
B2: 45, 23, 12, 56, 23, 86
L12: 4.2, 3.2, 1.2, 2.3, 2.3, 9.9
Then with that the following is done:
X = X.astype(int) # to make sure that the data is actually in int format.
Y = Y.astype(int)
X_train, X_test, y_train, y_test = train_test_split(X,Y,test_size=.10, random_state = 42)
xscaler = StandardScaler()
yscaler = StandardScaler()
pipe = Pipeline([('scaler', xscaler), ('logit', LogisticRegression())])
model = TransformedTargetRegressor(regressor=pipe, transformer=yscaler)
This however, throws out the following:
ValueError: Unknown label type: 'continuous'
Why does this happen even though the Y data is clearly binary valued?