Credit Score - Módulo 2: Boosting em python

Boost your machine learning skills with this comprehensive course! Learn about boosting, a powerful ensemble meta-algorithm used to significantly reduce both bias and variance in supervised learning models. This course explores various boosting techniques, including:

• Adaboosting: A foundational boosting algorithm.
• Gradient Boosting: A widely used technique for boosting performance.
• XGBoosting: A highly optimized and popular gradient boosting implementation known for its speed and accuracy.

You'll gain hands-on experience building a credit score model and compare its performance against a traditional logistic regression model. The course is designed for incremental learning, starting with the fundamental concepts of these algorithms before moving into practical application.

All coding is done in Python using Google Colab, so no installation is required – just focus on learning!

What is Boosting?

In machine learning, boosting addresses the question: Can a collection of weak learners be combined to create a single, strong learner? A weak learner is a classifier that performs only slightly better than random guessing. The core idea is to iteratively train weak learners, weighting them based on their performance, and combining them to form a more accurate model.

The development of boosting was significantly influenced by Robert Schapire's work in the early 1990s, who affirmatively answered the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?".

Freund and Schapire's arcing (Adapt[at]ive Resampling and Combining) is a general technique closely associated with boosting.

This course provides a practical understanding of boosting and its applications in real-world scenarios. Develop expertise in building high-performance predictive models with this in-demand machine learning technique.