class_docstr: This wrapper handles sklearn models for image classification.
class_docstr_tok: ["This","wrapper","handles","sklearn","models","for","image","classification","."]
docstr: Loads the model from the out dir.
docstr_tok: ["Loads","the","model","from","the","out","dir","."]
code: def _load(self):
      """Loads the model from the out dir."""
      self.model = joblib.load(self.out_dir / "model.joblib")
      self.scaler = joblib.load(self.out_dir / "scaler.joblib")
  
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 5 for calling the SklearnImageClassificationWrapper obj's underlying member method code with example usage: obj._load() without return types