Pragmatic Classification with Python

In my previous posting I wrote about classification basics, this posting will follow up and talk about Python tools for classification and give an example with one of the tools.

Open Source Python Tools for Classification

• Orange - machine learning tool which supports classification (including combining classifiers in ensembles), feature extraction, basic statistical analysis, regression and association rules. It also has an extension module which supports clustering and additional classifier algorithms. Note: Orange is probably the Python-based machine learning tool that is most similar to the more famous tool Weka (which is for Java, or Jython for that matter).

• Monte - less comprehensive than Orange, written purely in Python (i.e. no SWIGed C++). Looks interesting (has several classifiers algorithms), but the APIs seems to be in an early phase (relatively new tool in version 0.1.0)

• libsvm - Python API for most popular open source implementation of SVM. Note: libsvm is also included with Orange and PyML. (I used this tools during my PhD a few years ago)
  

• RPy - not exactly a classification tool, but it is quite useful with a statistics tool when you are doing classification (it has a nice plotting capability, not unlike matlabs), check out the demo.
  

• PyML - also less comprehensive than Orange (specialized towards classification and regression, it supports SVM/SMO, ANN and Ridge Regression), but it has a nice API. Example of use:
  

  
  from PyML import multi, svm, datafunc
  # read training data, last column has the class
  mydataset = datafunc.SparseDataSet('iris.data', labelsColumn = -1)
  myclassifier = multi.OneAgainstRest(svm.SVM())
  print "cross-validation results", myclassifier.cv(mydataset)
  

My recommendation is to either go with Orange or with PyML.

Pragmatic Classification: The very basics

Classification is an everyday task, it is about selecting one out of several outcomes based on their features. An example could be recycling of garbage where you select the bin based on the characteristics of the garbage, e.g. paper, metal, plastic or organic.

Classification with computers
For classification with computers the focus is frequently on the classifier - the function/algorithm that selects the class based on features (note: classifiers usually has to be trained to get fit for fight). Classifiers can be found in many flavors, and quite a few of them have impressive names (phrases with rough, kernel, vector, machine and reasoning aren't uncommon when naming them).

note: Garbage in leads to Garbage out - as (almost always) - same goes for classification.

The numerical baseline
Let us assume you have a data set with 1000 documents that shows to have 4 equally different categories (e.g. math, physics, chemistry and medicine). A simple classifier for a believe-to-be-similar-dataset could be the rule: "the class is math", which is likely to give a classification accuracy of about 25%. (Another classifier could be to pick a random category for every document). This can be used as a numerical baseline for comparison with when bringing in heavier classification machinery, e.g if you get 19% accuracy with the heavier machinery it probably isn't very good (or your feature representation isn't very good) for that particular problem. (Note: heavy classification machinery frequently has plenty of degrees of freedom, so fine tuning them can be a challenge, same goes for feature extraction and representation).

Combining classifiers
On the other hand, if the heavy machinery classifier gave 0% accuracy you could combine it with a random classifier to only randomly select from the 3 classes the heavy machinery classifier didn't suggest.

Question 1: What is the accuracy with these combined classifiers?

Baseline for unbalanced data sets
Quite frequently classification problems have to deal with unbalanced data sets, e.g. let us say you were to classify documents about soccer and casting (fishing), and your training data set contained about 99.99% soccer and 0.01% about casting, a baseline classifier for a similar dataset could be to say - "the article is about soccer". This would most likely be a very strong baseline, and probably hard to beat for most heavy machinery classifiers.

Silver bullet classifier and feature extraction method?

Q: My friend says that classifier algorithm X and feature extraction method Y are the best for all problems, is that the case?
A: No, tell him/her to read about the ugly duckling and no free lunch theorems which clearly says that there is no universally best classifier or feature extraction approach.

note: Just some of the basics this time, something more concrete next time (I think).

A Machine Learning Theory Dream Team

Russia is the birthplace of many great mathematicians, and the work from quite a few of them have significant impact on state-of-the-art computer science and machine learning theory, e.g.:

• Ludwig O. Hesse (1811-1874) - Hessian Matrix
• Used in calculations of Logistic Regression (which can be used for binary classification), and feed-forward Neural Networks

• David Hilbert (1862-1943) - Hilbert Space
• E.g. a feature space for Radial Basis Function kernels in Support Vector Machine classifiers can be described with a Hilbert Space

• Andrey N. Kolmogorov (1903-1987) - Kolmogorov Complexity
• Used in algorithmic information theory, and also in theory behind evolutionary and genetic programming

• Andrei A. Markov (1856-1922) - Markov Models and Markov Chains
  
• Can be used e.g. for simulation (in games).
  
• Noteworthy later "spinn-offs": Hidden Markov Models (HMM) and Markov Chain Monte Carlo (MCMC).

• Andrei N. Tikhonov (1906-1993) - Tikhonov Regularization
• Tikhonov Regularization is roughly a templating language for classification and regression. The template variable is a loss function, e.g. if using a square loss function you get Ridge Regression (also known as Regularized Least Squares Regression or Shrinkage Regression), an epsilon-insensitive loss function gives Support Vector Machine Regression, and a hinge loss function gives Support Vector Machine Classification.

Kudos.