You should start by converting your documents into TF-log(1 + IDF) vectors: term frequencies are sparse so you should use python dict with term as keys and count as values and then divide by total count to get the global frequencies.

Another solution is to use the abs(hash(term)) for instance as positive integer keys. Then you an use scipy.sparse vectors which are more handy and more efficient to perform linear algebra operation than python dict.

Also build the 150 frequencies vectors by averaging the frequencies of all the labeled documents belonging to the same category. Then for new document to label, you can compute the cosine similarity between the document vector and each category vector and choose the most similar category as label for your document.

If this is not good enough, then you should try to train a logistic regression model using a L1 penalty as explained in this example of scikit-learn (this is a wrapper for liblinear as explained by @ephes). The vectors used to train your logistic regression model should be the previously introduced TD-log(1+IDF) vectors to get good performance (precision and recall). The scikit learn lib offers a sklearn.metrics module with routines to compute those score for a given model and given dataset.

For larger datasets: you should try the vowpal wabbit which is probably the fastest rabbit on earth for large scale document classification problems (but not easy to use python wrappers AFAIK).

How big (number of words) are your documents? Memory consumption at 150K trainingdocs should not be an issue.

Naive Bayes is a good choice especially when you have many categories with only a few training examples or very noisy trainingdata. But in general, linear Support Vector Machines do perform much better.

Is your problem multiclass (a document belongs only to one category exclusivly) or multilabel (a document belongs to one or more categories)?

Accuracy is a poor choice to judge classifier performance. You should rather use precision vs recall, precision recall breakeven point (prbp), f1, auc and have to look at the precision vs recall curve where recall (x) is plotted against precision (y) based on the value of your confidence-threshold (wether a document belongs to a category or not). Usually you would build one binary classifier per category (positive training examples of one category vs all other trainingexamples which don t belong to your current category). You ll have to choose an optimal confidence threshold per category. If you want to combine those single measures per category into a global performance measure, you ll have to micro (sum up all true positives, false positives, false negatives and true negatives and calc combined scores) or macro (calc score per category and then average those scores over all categories) average.

We have a corpus of tens of million documents, millions of training examples and thousands of categories (multilabel). Since we face serious training time problems (the number of documents are new, updated or deleted per day is quite high), we use a modified version of liblinear. But for smaller problems using one of the python wrappers around liblinear (liblinear2scipy or scikit-learn) should work fine.

Is there a way to have a "none of the above" option for the classifier just in case the document doesn t fit into any of the categories?

You might get this effect simply by having a "none of the above" pseudo-category trained each time. If the max you can train is 5 categories (though I m not sure why it s eating up quite so much RAM), train 4 actual categories from their actual 2K docs each, and a "none of the above" one with its 2K documents taken randomly from all the other 146 categories (about 13-14 from each if you want the "stratified sampling" approach, which may be sounder).

Still feels like a bit of a kludge and you might be better off with a completely different approach -- find a multi-dimensional doc measure that defines your 300K pre-tagged docs into 150 reasonably separable clusters, then just assign each of the other yet-untagged docs to the appropriate cluster as thus determined. I don t think NLTK has anything directly available to support this kind of thing, but, hey, NLTK s been growing so fast that I may well have missed something...;-)