class_docstr: A model selector for classification.
  
  Parameters
  ----------
  models
  metric
class_docstr_tok: ["A","model","selector","for","classification",".","Parameters","--","--","--","--","--","models","metric"]
code: def _unit_test_params(cls):
      from river import compose, linear_model, optim, preprocessing
  
      yield {
          "models": [
              compose.Pipeline(
                  preprocessing.StandardScaler(),
                  linear_model.LogisticRegression(
                      optimizer=optim.SGD(lr=1e-2)
                  ),
              ),
              compose.Pipeline(
                  preprocessing.StandardScaler(),
                  linear_model.LogisticRegression(
                      optimizer=optim.SGD(lr=1e-1)
                  ),
              ),
          ],
          "metric": metrics.Accuracy(),
      }
      yield {
          "models": [
              compose.Pipeline(
                  preprocessing.StandardScaler(),
                  linear_model.LogisticRegression(
                      optimizer=optim.SGD(lr=lr)
                  ),
              )
              for lr in [1e-4, 1e-3, 1e-2, 1e-1]
          ],
          "metric": metrics.Accuracy(),
      }
  
file_path: river/river/model_selection/base.py
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from collections.abc import Iterator
  from river import base, metrics
label_desc: Use image node_id 2 for calling the ModelSelectionClassifier obj's underlying member method code with example usage: obj._unit_test_params(cls) without return types