Greetings learners! Prepare to immerse yourself in advanced text classification techniques as we explore an advanced ensemble method: the Gradient Boosting Classifier. By the end of this lesson, you will have a sound understanding of this ensemble method and also gain practical experience in applying it using Python and Scikit-learn.

First, let's review a few steps that should already be familiar: loading required libraries and preparing the dataset, which is the Reuters-21578 Text Categorization Collection here.

This code prepares the dataset, using CountVectorizer for feature extraction, LabelEncoder for changing categories into numeric format, and splitting our data into training and test sets.

Gradient Boosting Classifier is an ensemble learning technique that fine-tunes its accuracy iteratively by addressing the inaccuracies of prior models, predominantly employing decision trees as its weak learners. The process unfolds through several critical stages: