Predicting Heart Failure Using Machine Learning

Predicting Heart Failure Using Machine Learning

I predicted heart failure using Random Forest, XGBoost, Neural Network, and an ensemble of models in my previous article. In this post, I would like to go over XGBoost parameter optimization to increase the model’s accuracy.

I predicted heart failure using Random Forest, XGBoost, Neural Network, and an ensemble of models in my previous article. In this post, I would like to go over XGBoost parameter optimization to increase the model’s accuracy.

According to the official XGBoost website, XGBoost is defined as an optimized distributed gradient boosting library designed to be highly efficient, flexible, and portable. It implements machine learning algorithms under the gradient boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way.

XGBoost is very popular with participants of Kaggle competitions because it can achieve a very high model accuracy. The only problem with it is the number of parameters one has to optimize to get good results.

XGBoost has three types of parameters: general parameters, booster parameters, and task parameters. General parameters select which booster you are using to do boosting, commonly tree or linear model; booster parameters depend on which booster you have chosen; learning task parameters specify the learning task and the corresponding learning objective. A detailed description of all parameters can be found here.

Going over all parameters is beyond the scope of this article. Instead, I will concentrate on optimizing the following selected tree booster parameters to increase the accuracy of our XGBoost model:

  1. Parameters that help prevent overfitting (aliases are for XGBoost python sklearn wrapper that uses sklearn naming convention)

eta [default=0.3, range: [0,1], alias: learning_rate]

machine-learning xgboost optimization ai parameter

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