title: How could I change datatype to float64 so that sklearn can work on dataframe which has data greater than np.float32
body: <p>In my data set there are few data(i.e. <code>1.4619664882428694e+258</code>) which are greater than <code>float32</code> max value(<code>3.4028235e+38</code>). Now during fitting the model I am getting the below error: </p>
  
  <pre><code>Input contains NaN, infinity or a value too large for dtype('float32').
  </code></pre>
  
  <p>I tried below code: </p>
  
  <pre><code>df_features = pd.read_csv('data\df_features.csv')
  df_target = pd.read_csv('data\df_target.csv')
  
  X_train, X_test, y_train, y_test = train_test_split(df_features, df_target, test_size=.25, random_state=0)
  
  model = AdaBoostRegressor()
  
  try:
      model.fit(X_train, y_train)
      y_pred = model.predict(X_test)
      acc = r2_score(y_test, y_pred)
      print(acc)
  
  except Exception as error:
      print(error)
  </code></pre>
  
  <p>How can I solve this problem if I want to use the real data without normalizing? Is there any option so that i can set default data type to float64 for sklearn. If so then how? </p>