  It is already reported in the literature that the performance of a machine
learning algorithm is greatly impacted by performing proper Hyper-Parameter
optimization. One of the ways to perform Hyper-Parameter optimization is by
manual search but that is time consuming. Some of the common approaches for
performing Hyper-Parameter optimization are Grid search Random search and
Bayesian optimization using Hyperopt. In this paper, we propose a brand new
approach for hyperparameter improvement i.e. Randomized-Hyperopt and then tune
the hyperparameters of the XGBoost i.e. the Extreme Gradient Boosting algorithm
on ten datasets by applying Random search, Randomized-Hyperopt, Hyperopt and
Grid Search. The performances of each of these four techniques were compared by
taking both the prediction accuracy and the execution time into consideration.
We find that the Randomized-Hyperopt performs better than the other three
conventional methods for hyper-paramter optimization of XGBoost.
