Building Machine Learning Systems with Python - Richert, Coelho

Chapter 7
Fortunately, scikit-learn makes it very easy to do the right thing: it has classes named
LassoCV, RidgeCV, and ElasticNetCV, all of which encapsulate a cross-validation
check for the inner parameter. The code is 100 percent like the previous one, except
that we do not need to specify any value for alpha
from sklearn.linear_model import ElasticNetCV
met = ElasticNetCV(fit_intercept=True)
kf = KFold(len(target), n_folds=10)
for train,test in kf:
met.fit(data[train],target[train])
p = map(met.predict, data[test])
p = np.array(p).ravel()
e = p-target[test]
err += np.dot(e,e)
rmse_10cv = np.sqrt(err/len(target))
This results in a lot of computation, so you may want to get some coffee while
you are waiting (depending on how fast your computer is).
Rating prediction and recommendations
If you have used any commercial online system in the last 10 years, you have
probably seen these recommendations. Some are like Amazon's "costumers who
bought X also bought Y." These will be dealt with in the next chapter under the
topic of basket analysis. Others are based on predicting the rating of a product,
such as a movie.
This last problem was made famous with the Netflix Challenge; a million-dollar
machine learning public challenge by Netflix. Netflix (well-known in the U.S.
and U.K., but not available elsewhere) is a movie rental company. Traditionally,
you would receive DVDs in the mail; more recently, the business has focused on
online streaming of videos. From the start, one of the distinguishing features of the
service was that it gave every user the option of rating films they had seen, using
these ratings to then recommend other films. In this mode, you not only have the
information about which films the user saw, but also their impression of them
(including negative impressions).
In 2006, Netflix made available a large number of customer ratings of films in
its database and the goal was to improve on their in-house algorithm for ratings
prediction. Whoever was able to beat it by 10 percent or more would win 1 million
dollars. In 2009, an international team named BellKor's Pragmatic Chaos was able to
beat that mark and take the prize. They did so just 20 minutes before another team,
The Ensemble, passed the 10 percent mark as well! An exciting photo-finish for a
competition that lasted several years.
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