class_docstr: Benchmarks for LogisticRegression.
class_docstr_tok: ["Benchmarks","for","LogisticRegression","."]
code: def make_estimator(self, params):
      representation, solver, n_jobs = params
  
      penalty = "l2" if solver == "lbfgs" else "l1"
  
      estimator = LogisticRegression(
          solver=solver,
          penalty=penalty,
          multi_class="multinomial",
          tol=0.01,
          n_jobs=n_jobs,
          random_state=0,
      )
  
      return estimator
  
imports: from sklearn.linear_model import ElasticNet, Lasso, LinearRegression, LogisticRegression, Ridge, SGDRegressor
  from .common import Benchmark, Estimator, Predictor
  from .datasets import _20newsgroups_highdim_dataset, _20newsgroups_lowdim_dataset, _synth_regression_dataset, _synth_regression_sparse_dataset
  from .utils import make_gen_classif_scorers, make_gen_reg_scorers
label_desc: Use image node_id 3 for calling the LogisticRegressionBenchmark obj's underlying member method code with example usage: obj.make_estimator(params) and returns: estimator