class_docstr: This wrapper handles sklearn models for image classification.
class_docstr_tok: ["This","wrapper","handles","sklearn","models","for","image","classification","."]
docstr: Create the model based on self.model_name and store it in self.model.
docstr_tok: ["Create","the","model","based","on","self.model_name","and","store","it","in","self.model","."]
code: def _create_model(self) -> None:
      """Create the model based on self.model_name and store it in self.model."""
  
      # All config params not used anywhere else are passed on to the sklearn model.
      model_config = self.config.copy()
      model_config.pop("num_samples", None)
      model_config.pop("num_samples_to_plot", None)
  
      # Some models need probability=True so that we can predict the probability
      # further down.
      try:
          self.model = classifier_dict[self.model_name](
              probability=True, **model_config
          )
      except TypeError:  # no probability arg
          self.model = classifier_dict[self.model_name](**model_config)
  
imports: import sklearn.preprocessing
  from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
  from sklearn.gaussian_process import GaussianProcessClassifier
  from sklearn.linear_model import LogisticRegression, SGDClassifier, Perceptron, PassiveAggressiveClassifier
  from sklearn.naive_bayes import GaussianNB
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.neural_network import MLPClassifier
  from sklearn.svm import SVC, LinearSVC
  from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
  from sklearn.utils import shuffle
  import joblib
  import numpy
  from loguru import logger
  from ..model_wrapper import ModelWrapper
  from .None import preprocessing, visualization
label_desc: Use image node_id 1 for calling the SklearnImageClassificationWrapper obj's underlying member method code with example usage: obj._create_model() without return types