Why is XGBoost so popular?
XGBoost so popular
On this page
XGBoost
Following are the advantages of XGBoost that contribute to its popularity:
- Exception to Bias Variance Trade off rule i.e. bias and variance can be reduced at the same time
- Computationally cost effective as it allows multi threading and parallel processing
- Allows hardware optimizations
- Allows early stopping
- Built in regularization factor
- Unaffected by multicollinearity. This becomes even more relevant given the vulnerability of Decision Trees to multicollinearity.
- Robust to outliers
- Robust to missing values
- Adept to sparse dataset. Feature engineering steps like one-hot encoding and others make data sparse. XGBoost uses a sparsity aware split finding algorithm to that manages sparsity patterns in the data
- Handles noise adeptly in case of classification problem
- Works both for regression and classification problems
- Works with both categorical and continuous variables.
- Package is just 7 years old and new features are added everyday.