title: Preprocess large datafile with categorical and continuous features
body: <p>First thanks for reading me and thanks a lot if you can give any clue to help me solving this. </p>
  
  <p>As I'm new to Scikit-learn, don't hesitate to provide any advice that can help me to improve the process and make it more professional.</p>
  
  <p>My goal is to classify data between two categories. I would like to find a solution that would give me the most precise result. At the moment, I'm still looking for the most suitable algorithm and data preprocessing. </p>
  
  <p>In my data I have 24 values : 13 are nominal, 6 are binarized and the others are continuous. Here is an example of a line</p>
  
  <blockquote>
    <p>"RENAULT";"CLIO III";"CLIO III (2005-2010)";"Diesel";2010;"HOM";"_AAA";"_BBB";"_CC";0;668.77;3;"Fevrier";"_DDD";0;0;0;1;0;0;0;0;0;0;247.97</p>
  </blockquote>
  
  <p>I have around 900K lines for learning and I do my test over 100K lines</p>
  
  <p>As I want to compare several algorithm implementations, I wanted to encode all the nominal values so it can be used in several Classifier. </p>
  
  <p>I tried several things:</p>
  
  <ol>
  <li>LabelEncoder : this was quite good but it gives me ordered values that would be miss-interpreted by the classifier. </li>
  <li>OneHotEncoder : if I understand well, it is quite perfect for my needs because I could select the column to binarize. But as I have a lot of nominal values, it always goes in MemoryError. Moreover, its input must be numerical so it is compulsory to LabelEncode everything before.</li>
  <li>StandardScaler : this is quite useful but not for what I need. I decided to integrate it to scale my continuous values.</li>
  <li>FeatureHasher : first I didn't understand what it does. Then, I saw that it was mainly used for Text analysis. I tried to use it for my problem. I cheated by creating a new array containing the result of the transformation. I think it was not built to work that way and it was not even logical.</li>
  <li>DictVectorizer : could be useful but looks like OneHotEncoder and put even more data in memory.</li>
  <li>partial_fit : this method is given by only 5 classifiers. I would like to be able to do it with Perceptron, KNearest and RandomForest at least so it doesn't match my needs</li>
  </ol>
  
  <p>I looked on the documentation and found these information on the page <a href="http://scikit-learn.org/stable/modules/preprocessing.html#preprocessing" rel="noreferrer">Preprocessing</a> and <a href="http://scikit-learn.org/stable/modules/feature_extraction.html" rel="noreferrer">Feature Extraction</a>.</p>
  
  <p>I would like to have a way to encode all the nominal values so that they will not be considered as ordered. This solution can be applied on large datasets with a lot of categories and weak resources. </p>
  
  <p>Is there any way I didn't explore that can fit my needs?</p>
  
  <p>Thanks for any clue and piece of advice.</p>