Welcome to the lesson on Random Forest for Text Classification. As we continue our journey into the world of text classification techniques in Natural Language Processing (NLP), this lesson brings us to the powerful ensemble learning method - the Random Forest algorithm.

In this lesson, we will:

• Broaden our understanding of the Random Forest algorithm.
• Apply it using Python's scikit-learn package, on the SMS Spam Collection dataset.
• Evaluate our model's accuracy in classifying whether a text message is spam or not.

By the end of this lesson, you will have gained hands-on experience in implementing a Random Forest classifier, equipping you with another versatile tool in your NLP modeling toolkit.

Let the learning begin!

Before we dive into the nuances and application of the Random Forest algorithm, let's first load and preprocess our text data.

Remember, the CountVectorizer transforms the text data into vectors of token occurrence counts (also known as bag of words), which is required for processing by machine learning models. We also use a stratified train-test split to ensure a balanced representation of different classes within both our training and test data.