class_docstr: Evolutionary optimization of Neural Networks.
  
  Parameters:
  -----------
  n_individuals: int
      The number of neural networks that are allowed in the population at a time.
  mutation_rate: float
      The probability that a weight will be mutated.
  model_builder: method
      A method which returns a user specified NeuralNetwork instance. 
class_docstr_tok: ["Evolutionary","optimization","of","Neural","Networks",".","Parameters",":","--","--","--","--","--","-","n_individuals",":","int","The","number","of","neural","networks","that","are","allowed","in","the","population","at","a","time",".","mutation_rate",":","float","The","probability","that","a","weight","will","be","mutated",".","model_builder",":","method","A","method","which","returns","a","user","specified","NeuralNetwork","instance","."]
docstr: Returns a new individual 
docstr_tok: ["Returns","a","new","individual"]
code: def _build_model(self, id):
      """Returns a new individual"""
      model = self.model_builder(
          n_inputs=self.X.shape[1], n_outputs=self.y.shape[1]
      )
      model.id = id
      model.fitness = 0
      model.accuracy = 0
  
      return model
  
file_path: ML-From-Scratch/mlfromscratch/supervised_learning/neuroevolution.py
imports: from __future__ import print_function, division
  import numpy
  import copy
label_desc: Use image node_id 2 for calling the Neuroevolution obj's underlying member method code with example usage: obj._build_model(id) and returns: model