def __init__(self, **kwargs):
    'Initialize RandomForestClassifier instance.\n        '
    warnings.filterwarnings(action='ignore', category=ChangedBehaviorWarning)
    warnings.filterwarnings(action='ignore', category=ConvergenceWarning)
    warnings.filterwarnings(action='ignore', category=DataConversionWarning)
    warnings.filterwarnings(action='ignore', category=DataDimensionalityWarning)
    warnings.filterwarnings(action='ignore', category=EfficiencyWarning)
    warnings.filterwarnings(action='ignore', category=FitFailedWarning)
    warnings.filterwarnings(action='ignore', category=NonBLASDotWarning)
    warnings.filterwarnings(action='ignore', category=UndefinedMetricWarning)
    self._params = dict(n_estimators=ParameterDefinition(MinMax(min=10, max=111), np.uint))
    self.__random_forest_classifier = RF()
Initialize RandomForestClassifier instance.
def __init__(self, **kwargs):
    '\n        '
    warnings.filterwarnings(action='ignore', category=ChangedBehaviorWarning)
    warnings.filterwarnings(action='ignore', category=ConvergenceWarning)
    warnings.filterwarnings(action='ignore', category=DataConversionWarning)
    warnings.filterwarnings(action='ignore', category=DataDimensionalityWarning)
    warnings.filterwarnings(action='ignore', category=EfficiencyWarning)
    warnings.filterwarnings(action='ignore', category=FitFailedWarning)
    warnings.filterwarnings(action='ignore', category=NonBLASDotWarning)
    warnings.filterwarnings(action='ignore', category=UndefinedMetricWarning)
    self._params = dict(n_estimators=ParameterDefinition(MinMax(min=10, max=111), np.uint))
    self.__random_forest_classifier = RF()