text: import numpy as np
  from sklearn.datasets import load_breast_cancer
  from sklearn.linear_model import LogisticRegression
  from sklearn.metrics import accuracy_score , confusion_matrix
  from sklearn.preprocessing import FunctionTransformer
  from sklearn.model_selection import train_test_split
  
  data = load_breast_cancer()
  X = data.data
  y = data.target
  
  def function1(z):
  #    return np.log1p(z)
  #    return np.sqrt(z)
      return np.power(z,4)
  
  
  f = FunctionTransformer(func = function1)
  f.fit(X)
  x_f = f.transform(X)
  
  
  x_train, x_test, y_train, y_test = train_test_split(x_f, y, test_size = 0.2)
  
  
  logreg = LogisticRegression()
  logreg.fit(x_train , y_train)
  result= logreg.predict(x_test)
  print(accuracy_score(y_test , result))
   
  conf = confusion_matrix(y_test , result)
  print('confusion matrix \n',  conf)
  
  