I'm trying to get familiar with the sklearn library, and now I'm trying to implement logistic regression for a dataframe containing numerical and categorical values to predict a binary target variable.
While reading some documentation I found the logistic regression should be used to predict binary variables presented by 0 and 1.
My target variable is "YES" and "NO", should I code it to 0 and 1 for the algorithm to work properly, or there is no difference?
Maybe I just didn't get the idea but can someone confirm this to me.


The string labels work just fine, here is an example:

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
import numpy
X, y = load_iris(return_X_y=True)
y_string = numpy.array(['YES' if label == 1 else 'NO' for label in y])
clf = LogisticRegression(random_state=0, solver='lbfgs', multi_class='multinomial').fit(X, y_string)
y_pred = clf.predict(X[50:100, :])


['NO' 'NO' 'NO' 'YES' 'NO' 'YES' 'NO' 'YES' 'NO' 'NO' 'YES' 'NO' 'YES'
 'NO' 'NO' 'NO' 'NO' 'YES' 'YES' 'YES' 'NO' 'NO' 'YES' 'YES' 'NO' 'NO'
 'YES' 'NO' 'NO' 'YES' 'YES' 'YES' 'YES' 'YES' 'NO' 'NO' 'NO' 'YES' 'NO'
 'YES' 'YES' 'NO' 'YES' 'YES' 'YES' 'NO' 'NO' 'NO' 'YES' 'NO']

Yo can replace y_string to y for the numerical example.

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