title: Sklearn or my computer can't handle data set
body: <p>I have a data set of 100,000 rows and 125 features and continuous output. I'm running numpy, pandas, and sklearn on 2013 macbook pro with 16gb ram. Using only 10,000 rows and 5 features to train, the ipython kernel dies when I try to predict only 1,000 rows in the test set using sklearn RandomForestClassifier. Should I be hitting this limit with only 10,000 x 5 to train and 1,000 x 5 to predict?</p>
  
  <p><a href="https://www.kaggle.com/c/allstate-claims-severity/data" rel="nofollow">data</a></p>
  
  <pre><code>import pandas as pd
  import numpy as np 
  import sklearn as sk
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.feature_selection import SelectKBest
  
  df = pd.read_csv('train.csv')
  df2 = pd.read_csv('test.csv') 
  cat = df.iloc[:,:117]
  cont = df.iloc[:,117:df.shape[1]-1]
  y = df.iloc[:,df.shape[1]-1]
  cat2 = df2.iloc[:,:117]
  cont2 = df2.iloc[:,117:df2.shape[1]]
  
  cont_new = SelectKBest(k=5).fit_transform(cont,y)
  test = cont2.iloc[:,[6,7,10,12,13]]
  rf = RandomForestClassifier(n_jobs=-1,n_estimators=100,max_depth=20)
  y = np.asarray(y,dtype="|S6")
  rf.fit(cont_new[:10000], y[:10000])
  predictions = rf.predict(test[:5000])
  print(predictions)
  </code></pre>