Credit Score - Módulo 2: Boosting em python

Neste curso é apresentado a técnica de boosting.São exploradas diferentes tipos de implementações como Adaboosting, Gradiente Boosting e XGBoosting.É realizada a construção de um credit score e a comparação da performance com o modelo de regressão logística.O conteúdo é pensado para ser construído de forma incremental em que o aluno é convidado a pensar sobre os fundamentos dos algoritmos e depois é apresentado para a prática.A prática é realizada em python, com o uso do Google Colab. Você não precisará instalar nada!In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing). In contrast, a strong learner is a classifier that is arbitrarily well-correlated with the true classification.Robert Schapire's affirmative answer in a 1990 paper to the question of Kearns and Valiant has had significant ramifications in machine learning and statistics, most notably leading to the development of boosting.When first introduced, the hypothesis boosting problem simply referred to the process of turning a weak learner into a strong learner. "Informally, [the hypothesis boosting] problem asks whether an efficient learning algorithm […] that outputs a hypothesis whose performance is only slightly better than random guessing [i.e. a weak learner] implies the existence of an efficient algorithm that outputs a hypothesis of arbitrary accuracy [i.e. a strong learner]." Algorithms that achieve hypothesis boosting quickly became simply known as "boosting". Freund and Schapire's arcing (Adapt[at]ive Res