When working with the majority of machine learning algorithms (in particular, supervised ones), it's important to train the model with a dataset containing almost the same number of elements for each class. Remember that our goal is training models that can generalize in the best way for all of the possible classes and supposes that we have a binary dataset containing 1,000 samples with a proportion (0.95, 0.05). There are many scenarios where this proportion is very common. For example, a spam detector can collect lots of spam emails, but it's much more difficult to have access to personally accepted emails. However, we can suppose that some users (a very small percentage) decided to share anonymous regular messages so that our dataset consists of 5% non-spam entries.
Now, let's consider a static algorithm that always outputs the label 0 (for example...
