Introduction K-Means is an example of a clustering algorithm. Clustering is a fundamental concept in Machine Learning, where the goal is to group a set of objects so that objects in the same group are more similar to each other than to those in other groups. Clustering belongs to the set of unsupervised Machine Learning algorithms, that is no ground truth is needed. Among the various clustering algorithms, K-Means stands out for its simplicity and efficiency.

Introduction Gradient Boosting is an ensemble model of a sequential series of shallow Decision Trees. The single trees are weak learners with little predictive skill, but together, they form a strong learner with high predictive skill. For a more detailed explanation, please refer to the post Gradient Boosting for Regression - Explained. In this article, we will discuss different implementations of Gradient Boosting. The focus is to give a high-level overview of different implementations and discuss the differences.

Introduction In this post, we develop a Gradient Boosting model for a binary classification. We focus on the calculations of each single step for a specific example chosen. For a more general explanation of the algorithm and the derivation of the formulas for the individual steps, please refer to Gradient Boost for Classification - Explained and Gradient Boost for Regression - Explained. Additionally, we show a simple example of how to apply Gradient Boosting for classification in Python.