code: def trees(self, quantile=0.8):
      """Extra Trees based reduction strategy. Like 'forrest', somewhat more
      aggressive than for example 'spearman' because there are no
      negative values, but instead the highest positive correlation
      is minused from all the values so that max value is 0, and then
      values are turned into positive. The one with the highest positive
      score in the end will be dropped. This means that anything with
      0 originally, is a candidate for dropping. Because there are multiple
      zeroes in many cases, there is an element of randomness on which one
      is dropped.
  
      """
  
      import wrangle
      import numpy as np
  
      # handle conversion to multi_labels
      from .reduce_utils import cols_to_multilabel
  
      data = cols_to_multilabel(self)
  
      # because extra trees wants label as 'y' we first transform with quantile
      quantile_value = data[self.reduction_metric].quantile(quantile)
      data[self.reduction_metric] = (
          data[self.reduction_metric] > quantile_value
      )
  
      # get the correlations
      corr_values = wrangle.df_corr_extratrees(
          data, self.reduction_metric
      )
  
      # drop labels where value is NaN
      corr_values.dropna(inplace=True)
  
      # handle the turning around of values (see docstring for more info)
      corr_values -= corr_values[0]
      corr_values = corr_values.abs()
  
      # get the strongest correlation
      corr_values = corr_values.index[-1]
  
      # get the label, value, and dtype from the column header
      label, dtype, value = corr_values.split("~")
  
      # convert things back to their original dtype
      value = np.array([value]).astype(dtype)[0]
  
      # this is where we modify the parameter space accordingly
      self.param_object.remove_is(label, value)
  
      return self
  
label_desc: Use image node_id 1 for calling a global function with example usage: trees(quantile) and returns: self