Gradient Boosted Decision Trees explained with a real-life example and some Python code

Gradient Boosting algorithms tackle one of the biggest problems in Machine Learning: bias. Decision Trees is a simple and flexible algorithm. An underfit Decision Tree has low depth, meaning it splits the dataset only a few of times in an attempt to separate the data. Because it doesn't separate the dataset into more and more distinct observations, it can't capture the true patterns in it. When it comes to tree-based algorithms Random Forests was revolutionary, because it used Bagging to reduce the overall variance of the model with an ensemble of random trees.