Building Machine Learning Systems with Python - Richert, Coelho

Chapter 6
° ° Experiment with whether or not to use the logarithm of the word
counts (sublinear_tf)
° ° Experiment with whether or not to track word counts or simply track
whether words occur or not by setting binary to True or False
• MultinomialNB
° ° Decide which of the following smoothing methods to use by
setting alpha:
° ° Add-one or Laplace smoothing: 1
° ° Lidstone smoothing: 0.01, 0.05, 0.1, or 0.5
° ° No smoothing: 0
A simple approach could be to train a classifier for all those reasonable exploration
values while keeping the other parameters constant and checking the classifier's
results. As we do not know whether those parameters affect each other, doing it
right would require that we train a classifier for every possible combination of all
parameter values. Obviously, this is too tedious for us to do.
Because this kind of parameter exploration occurs frequently in machine learning
tasks, scikit-learn has a dedicated class for it called GridSearchCV. It takes an
estimator (an instance with a classifier-like interface), which would be the pipeline
instance in our case, and a dictionary of parameters with their potential values.
GridSearchCV expects the dictionary's keys to obey a certain format so that it is able
to set the parameters of the correct estimator. The format is as follows:
____...__
Now, if we want to specify the desired values to explore for the min_df parameter of
TfidfVectorizer (named vect in the Pipeline description), we would have to say:
Param_grid={"vect__ngram_range"=[(1, 1), (1, 2), (1, 3)]}
This would tell GridSearchCV to try out unigrams, bigrams, and trigrams as
parameter values for the ngram_range parameter of TfidfVectorizer.
Then it trains the estimator with all possible parameter/value combinations. Finally,
it provides the best estimator in the form of the member variable best_estimator_.
As we want to compare the returned best classifier with our current best one, we
need to evaluate it the same way. Therefore, we can pass the ShuffleSplit instance
using the CV parameter (this is the reason CV is present in GridSearchCV).
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