title: Can I input strings into "features" for DecisionTreeClassifiers for scikit-learn?
body: <p>I am following the Google Dev Machine Learning Recipe. I am trying to use this ML algorithm to help me solve a problem faced at work. We often get a variety of different formats for dates when performing ETL, and would like to be able to identify certain rows as dates. </p>
  
  <p>The current solution that works is Regex, I would like to use ML to train the computer to identify dates as well.</p>
  
  <p>The code I am referring to from Google Dev is this (Object Recognition):</p>
  
  <pre><code>import sklearn
  from sklearn import tree
  #Featuers:  0 = "bumpy" 1 = "smooth"
  #Labels:    0 = apple 1 = orange
  features = [[140, 1], [130, 1], [150, 0], [170, 0]]
  labels = [0, 0, 1, 1]
  
  # We will be using a Decision Tree in this instance
  clf = tree.DecisionTreeClassifier()
  
  #fit = This is the training algorithm, this helps identify patterns as 
  to what attributes are associated with apples etc
  
  clf = clf.fit(features, labels)
  
  print(clf.predict([[160, 0]])) #This outputs 1, so it believes it is an orange. 
  </code></pre>
  
  <p>I want to load an entire column of different date types (12/12/12, Dec-12-12, etc) and different strings (12 12 12, user/Documents/Python etc). </p>
  
  <p>The next column is going to be of type string (converted into 0 and 1 in code as variable "labels"): 1 = if the string is a date, 0 = if the string is just a string.</p>
  
  <p>Hopefully I am on the right track of thought. </p>