class_docstr: Benchmarks for Ridge.
class_docstr_tok: ["Benchmarks","for","Ridge","."]
code: def make_data(self, params):
      representation, solver = params
  
      if representation == "dense":
          data = _synth_regression_dataset(
              n_samples=500000, n_features=100
          )
      else:
          data = _synth_regression_sparse_dataset(
              n_samples=100000, n_features=10000, density=0.005
          )
  
      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 RidgeBenchmark obj's underlying member method code with example usage: obj.make_data(params) and returns: data