So in this lecture, I will be giving you an official exercise prompt in preparation for the next lecture.
As with the other exercises in this course, please feel free to look at the official notebook in order
to get the data set.
But please do not cheat by looking at the whole solution.
So the exercise can be described as quite simply, what you were going to get is a data set of SMS messages
which are labeled as spam or not spam.
Your job, of course, is to build a classifier and assess its accuracy on both the training and test
sets.
Note that because the data set is just a single file, you will need to split the data into train and
test yourself.
So let's go through some additional details that may help you complete the exercise.
Firstly, note that it will be your choice, which factorization strategy you want to use, as you recall.
This will include tokenisation as well.
You may choose the count riser with default settings or even TF IDF with stop words and limitation and
normalization and so forth.
So that is up to you as your classifier, you should choose an appropriate form of niveis, either writing
it yourself or using Saikia.
Learn depending on how advanced do you want to go.
Furthermore, you should feel free to try other classifiers as well.
Finally, you'll want to check the performance of your model.
Note that by default, when you call the score function inside can learn this returns the accuracy.
However, recall that this is not an ideal scoring function when the classes are imbalanced.
Thus, you should check whether the classes are imbalanced in order to determine if other scoring functions
are necessary to use some examples of scoring functions that take into account class imbalance or the
F1 score in the AUC.