Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty-CodePudding

I'm building a logistic regression model to predict a binary target feature. I want to try different values of different parameters using the param_grid argument, to find the best fit with the best values. This is my code:

from sklearn.model_selection import train_test_split

X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.25, random_state = 42)

logModel = LogisticRegression(C = 1, penalty='l1',solver='liblinear');

Grid_params = {
    "penalty" : ['l1','l2','elasticnet','none'],
    "C" : [0.001, 0.01, 0.1, 1, 10, 100, 1000], # Basically smaller C specify stronger regularization.
    'solver' : ['lbfgs','newton-cg','liblinear','sag','saga'],
    'max_iter' : [50,100,200,500,1000,2500]
    
}

clf = GridSearchCV(logModel, param_grid=Grid_params, cv = 10, verbose = True, n_jobs=-1,error_score='raise')

clf_fitted = clf.fit(X_train,Y_train)

And this is where I get the error. I have read already that some solvers dont work with l1, and some don't work with l2. How can I tune the param_grid in this case?

I tried also using only simple logModel = LogisticRegression() but didn't work.

Full error:

ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.

CodePudding user response：

Gridsearch accepts the list of dicts for that purpose, given you absolutely need to include solvers into grid, you should be able to do something like this:

Grid_params  = [
    {'solver' : ['saga'],
      'penalty' : ['elasticnet', 'l1', 'l2', 'none'],
      'max_iter' : [50,100,200,500,1000,2500],
      'C' : [0.001, 0.01, 0.1, 1, 10, 100, 1000]},
    {'solver' : ['newton-cg', 'lbfgs'],
      'penalty' : ['l2','none'],
      'max_iter' : [50,100,200,500,1000,2500],
      'C' : [0.001, 0.01, 0.1, 1, 10, 100, 1000]},
      ...
    ]