Why is XGBoost so powerful?

XGBOOST – Why is it so Important? In broad terms, it’s the efficiency, accuracy, and feasibility of this algorithm . It has both linear model solver and tree learning algorithms. So, what makes it fast is its capacity to do parallel computation on a single machine.

XGBoost is still a great choice for a wide variety of real-world machine learning problems . Neural networks, especially recurrent neural networks with LSTMs are generally better for time-series forecasting tasks. There is “no free lunch” in machine learning and every algorithm has its own advantages and disadvantages.

As XGBoost is very good at identifying patterns in data, if you have enough temporal features describing your dataset, it will provide very decent predictions .

XGBoost is faster than the LSTM method with equal precision in the correct tuning parameters . The drawback is its feature-importance is not so accuracy as LSTM+SHAP combination.

When to NOT use XGBoost

There are multiple multivariate forecasting methods available like — Pmdarima, VAR, XGBoost etc. In this blog, we’ll focus on the XGBoost (Extreme Gradient Boosting) regression method only . First we’ll use AR (AutoRegressive) model to forecast individual independent external drivers.3 Şub 2022