Extreme Gradient Boost Regression (XGBoost)

Extreme Gradient Boost Regression (XGBoost)

Extreme Gradient Boost Regression (XGBoost)

Description

Extreme Gradient Boost (XGBoost) Regression is a Decision tree-based ensemble algorithm that uses a gradient boosting framework.

Why to use

Predictive Modeling 

When to use

When high execution speed and model performance is required.

When not to use

On textual data.

Prerequisites

  • If the data contains any missing values, use Missing Value Imputation before proceeding with XGBoost Regression.
  • If the input variable is of categorical type, use Label Encoder.
  • The output variable must be a continuous data type.

Input

Any continuous data.

Output

The predicted value of the dependent variable.

Statistical Methods used

Dimensionality Reduction

Limitations

It cannot be used on textual data.


XGBoost is the abbreviation of EXtreme Gradient Boost. It is an ensemble model that combines different machine learning models to get a single prediction. XGBoost turns weak learning models into strong learning models by focusing on the errors in the individual models. In XGBoost, the individual models train upon the residuals (Predicted Result – Actual Result) in each boosting round. XGBoost focuses on speed enhancements using parallel computing and cache mechanism.  

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