def perform_randomized_search(features, target):
  '''Performs a randomized search on Logistic regression'''
  import numpy as np
  from sklearn.linear_model import LogisticRegression
  from sklearn.model_selection import RandomizedSearchCV
  from scipy.stats import uniform
  # Create logistic regression
  model = LogisticRegression()
  # Create range of candidate penalty values
  penalty = ['l1', 'l2']
  # Create distribution of candidate regularization values
  C = uniform(loc=0, scale=4)
  # Create hyperparameter options
  hyperparameters = dict(C=C, penalty=penalty)

  # Create randomized search
  randomizedsearch = RandomizedSearchCV(model, hyperparameters, random_state=1, n_iter=100, cv=5, verbose=1)
  # Fit randomized search
  best_model = randomizedsearch.fit(features, target)
  return best_model