code: def get_fast_classifiers(n_classes):
      """Get a list of very fast classifiers.
  
      Parameters
      ----------
      n_classes : int
          Number of classes in the dataset. Used to decide on the complexity
          of some of the classifiers.
  
  
      Returns
      -------
      fast_classifiers : list of sklearn estimators
          List of classification models that can be fitted and evaluated very
          quickly.
      """
      return [
          # These are sorted by approximate speed
          DummyClassifier(strategy="prior"),
          GaussianNB(),
          make_pipeline(MinMaxScaler(), MultinomialNB()),
          DecisionTreeClassifier(max_depth=1, class_weight="balanced"),
          DecisionTreeClassifier(
              max_depth=max(5, n_classes), class_weight="balanced"
          ),
          DecisionTreeClassifier(
              class_weight="balanced", min_impurity_decrease=0.01
          ),
          LogisticRegression(
              C=0.1,
              solver="lbfgs",
              multi_class="auto",
              class_weight="balanced",
              max_iter=1000,
          ),
          # FIXME Add warm starting here?
          LogisticRegression(
              C=1,
              solver="lbfgs",
              multi_class="auto",
              class_weight="balanced",
              max_iter=1000,
          ),
      ]
  
file_path: dabl/dabl/pipelines.py
imports: from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.naive_bayes import GaussianNB, MultinomialNB
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.pipeline import make_pipeline
  from sklearn.linear_model import LogisticRegression, Ridge, Lasso
  from .portfolios.portfolio_base import portfolio_base
  from .portfolios.portfolio_mixed import portfolio_mixed
  from .portfolios.portfolio_hgb import portfolio_hgb
  from .portfolios.portfolio_svc import portfolio_svc
  from .portfolios.portfolio_rf import portfolio_rf
  from .portfolios.portfolio_lr import portfolio_lr
label_desc: Use image node_id 1 for calling a global function with example usage: get_fast_classifiers(n_classes) and returns: list