class_docstr: Loss function used in binary classification tasks.
class_docstr_tok: ["Loss","function","used","in","binary","classification","tasks","."]
code: def compute_derivatives(self, y_true, y_pred):
      y_trs = self.transfer(y_pred)
  
      return GradHess(y_trs - y_true, y_trs * (1.0 - y_trs))
  
file_path: river/river/tree/losses.py
imports: from __future__ import annotations
  import abc
  import math
  from .utils import GradHess
label_desc: Use image node_id 1 for calling the BinaryCrossEntropyLoss obj's underlying member method code with example usage: obj.compute_derivatives(y_true, y_pred) and returns: GradHess