Learning Data Mining with Python

Next Steps…
Deeper networks
These techniques will probably fool our current implementation, so improvements
will need to be made to make the method better. Try some of the deeper networks
we used in Chapter 11, Classifying Objects in Images Using Deep Learning.
Larger networks need more data, though, so you will probably need to generate
more than the few thousand samples we did in this chapter in order to get good
performance. Generating these datasets is a good candidate for parallelization—lots
of small tasks that can be performed independently.
Reinforcement learning
http://pybrain.org/docs/tutorial/reinforcement-learning.html
Reinforcement learning is gaining traction as the next big thing in data mining—
although it has been around a long time! PyBrain has some reinforcement learning
algorithms that are worth checking out with this dataset (and others!).
Chapter 9 – Authorship Attribution
Increasing the sample size
The Enron application we used ended up using just a portion of the overall dataset.
There is lots more data available in this dataset. Increasing the number of authors
will likely lead to a drop in accuracy, but it is possible to boost the accuracy further
than was achieved in this chapter, using similar methods. Using a Grid Search, try
different values for n-grams and different parameters for support vector machines,
in order to get better performance on a larger number of authors.
Blogs dataset
The dataset used in Chapter 12, Working with Big Data, provides authorship-based
classes (each blogger ID is a separate author). This dataset can be tested using
this kind of method as well. In addition, there are the other classes of gender, age,
industry, and star sign that can be tested—are authorship-based methods good for
these classification tasks?
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