The penalty terms are regularisation terms that affect the training process and thus the decision boundary found (i.e. weights and/or bias). Once trained, the prediction is not affected by neither the regularisation method nor the loss function. So, any linear classifier/regressor is still linear after penalisation. That said, since logistic regression classifier is a linear model, it is still linear when penalised.

The penalty terms are regularisation terms that affect the training process and thus the decision boundary found (i.e. weights and/or bias). Once trained, the prediction is not affected by neither the regularisation method nor the loss function. So, any linear classifier/regressor is still linear after penalisation. That said, since logistic regression classifier has a linear decision boundary, it'll still have a linear decision boundary when penalised.

The penalty terms are regularisation terms that affect the training process and thus found decision boundary (i.e. weights and/or bias). Once trained, the prediction is not affected by neither the regularisation method nor the loss function. So, any linear classifier/regressor is still linear after penalisation. That said, since logistic regression classifier is a linear model, it is still linear when penalised.

The penalty terms are regularisation terms that affect the training process and thus the decision boundary found (i.e. weights and/or bias). Once trained, the prediction is not affected by neither the regularisation method nor the loss function. So, any linear classifier/regressor is still linear after penalisation. That said, since logistic regression classifier is a linear model, it is still linear when penalised.