content: """
  ======================================================
  Imputing missing values before building an estimator
  ======================================================
  
  This example shows that imputing the missing values can give better results
  than discarding the samples containing any missing value.
  Imputing does not always improve the predictions, so please check via cross-validation.
  Sometimes dropping rows or using marker values is more effective.
  
  Missing values can be replaced by the mean, the median or the most frequent
  value using the ``strategy`` hyper-parameter.
  The median is a more robust estimator for data with high magnitude variables
  which could dominate results (otherwise known as a 'long tail').
  
  Script output::
  
    Score with the entire dataset = 0.56
    Score without the samples containing missing values = 0.48
    Score after imputation of the missing values = 0.55
  
  In this case, imputing helps the classifier get close to the original score.
    
  """
  import numpy as np
  
  from sklearn.datasets import load_boston
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import Imputer
  from sklearn.model_selection import cross_val_score
  
  rng = np.random.RandomState(0)
  
  dataset = load_boston()
  X_full, y_full = dataset.data, dataset.target
  n_samples = X_full.shape[0]
  n_features = X_full.shape[1]
  
  # Estimate the score on the entire dataset, with no missing values
  estimator = RandomForestRegressor(random_state=0, n_estimators=100)
  score = cross_val_score(estimator, X_full, y_full).mean()
  print("Score with the entire dataset = %.2f" % score)
  
  # Add missing values in 75% of the lines
  missing_rate = 0.75
  n_missing_samples = np.floor(n_samples * missing_rate)
  missing_samples = np.hstack((np.zeros(n_samples - n_missing_samples,
                                        dtype=np.bool),
                               np.ones(n_missing_samples,
                                       dtype=np.bool)))
  rng.shuffle(missing_samples)
  missing_features = rng.randint(0, n_features, n_missing_samples)
  
  # Estimate the score without the lines containing missing values
  X_filtered = X_full[~missing_samples, :]
  y_filtered = y_full[~missing_samples]
  estimator = RandomForestRegressor(random_state=0, n_estimators=100)
  score = cross_val_score(estimator, X_filtered, y_filtered).mean()
  print("Score without the samples containing missing values = %.2f" % score)
  
  # Estimate the score after imputation of the missing values
  X_missing = X_full.copy()
  X_missing[np.where(missing_samples)[0], missing_features] = 0
  y_missing = y_full.copy()
  estimator = Pipeline([("imputer", Imputer(missing_values=0,
                                            strategy="mean",
                                            axis=0)),
                        ("forest", RandomForestRegressor(random_state=0,
                                                         n_estimators=100))])
  score = cross_val_score(estimator, X_missing, y_missing).mean()
  print("Score after imputation of the missing values = %.2f" % score)
  