title: Label Encoding using weights for string nominal variables for random forest classification
body: <p>I have NYC 311 complaint dataset. I want to build a <strong>random forest classifier</strong> which will <strong>take categorical input features about a complaint and will determine the complaint type.</strong>
  Following are the input feature of a given complaint record</p>
  <pre><code>X = df[['Location Type', 'Incident Zip', 'Street Name', 
  'City', 'Borough', 'Open Data Channel Type']]
  </code></pre>
  <p>all of these <strong>features are nominal variables</strong>(categorical)
  I will need to convert the string variables into float ones before feeding them to the model. I am reluctant to use one hot label encoding since some features has more than 1000 categories and further computation might be out of reach of my laptop.</p>
  <p>I was thinking of using the <strong>weights of each variable (count of particular category/ total count)</strong> instead of the nominal string variables? will it be a good strategy?</p>