class_docstr: Benchmarks for LogisticRegression.
class_docstr_tok: ["Benchmarks","for","LogisticRegression","."]
code: def make_data(self, params):
      representation, solver, n_jobs = params
  
      if Benchmark.data_size == "large":
          if representation == "sparse":
              data = _20newsgroups_highdim_dataset(n_samples=10000)
          else:
              data = _20newsgroups_lowdim_dataset(n_components=1e3)
      else:
          if representation == "sparse":
              data = _20newsgroups_highdim_dataset(n_samples=2500)
          else:
              data = _20newsgroups_lowdim_dataset()
  
      return data
  
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 2 for calling the LogisticRegressionBenchmark obj's underlying member method code with example usage: obj.make_data(params) and returns: data