class_docstr: Wrapper to run an algorithm with multiple dataset sizes
  and compute accuracy and speedup of cuml relative to sklearn
  baseline.
class_docstr_tok: ["Wrapper","to","run","an","algorithm","with","multiple","dataset","sizes","and","compute","accuracy","and","speedup","of","cuml","relative","to","sklearn","baseline","."]
code: def __init__(
      self,
      bench_rows,
      bench_dims,
      dataset_name="blobs",
      input_type="numpy",
      test_fraction=0.10,
      n_reps=1,
  ):
      super().__init__(
          bench_rows, bench_dims, dataset_name, input_type, n_reps
      )
      self.test_fraction = test_fraction
  
file_path: cuml/python/cuml/benchmark/runners.py
imports: from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.benchmark import datagen
  from cuml.common.device_selection import using_device_type
  import warnings
  import time
  import itertools
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 1 to create a new AccuracyComparisonRunner object from inherited base classes: SpeedupComparisonRunner with example: obj = AccuracyComparisonRunner(bench_rows, bench_dims, dataset_name, input_type, test_fraction, n_reps)