Hyper-parameter Tuning of Machine Learning Models09 Jul 2018
To improve the performance of a machine learning model, one of the aspects that Data Scientists focus on is, tuning and fine-tuning hyper-parameters of Machine Learning (ML) models, besides working on Feature Handling and Model Ensemble. Parameter tuning plays a vital role in achieving higher accuracy of an ML model.
The initial accuracy of XGBoost model, from the above PDF document, is 73.26% with random parameters. After tuning 6 different parameters, the accuracy increased by 1.16% to 74.42%. Though the increase in accuracy is marginal due to the very small dataset, this document explains how one can tune hyper-parameters using GridSearchCV and improve the performance.