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We can see from the scores above that our Naive Bayes model actually does a pretty good job of classifying spam and “ham.” However, let’s take a look at a few additional models to see if we can’t improve anyway.
Specifically in this notebook, we will take a look at the following techniques:
Another really useful guide for ensemble methods can be found .
These ensemble methods use a combination of techniques you have seen throughout this lesson:
In this notebook, let’s get some practice with these methods, which will also help you get comfortable with the process used for performing supervised machine learning in Python in general.
Since you cleaned and vectorized the text in the previous notebook, this notebook can be focused on the fun part - the machine learning part.
In general, there is a five step process that can be used each time you want to use a supervised learning method (which you actually used above):
Follow the steps through this notebook to perform these steps using each of the ensemble methods: BaggingClassifier, RandomForestClassifier, and AdaBoostClassifier.
Step 1: First use the documentation to
import
all three of the models.
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