How to parallelise execution of XGBoost and Cross Validation in Python
XGBoost is a powerful and popular library for gradient boosting in Python. Cross-validation is a technique that is used to evaluate the performance of a machine learning model by dividing the data into subsets and training the model on different subsets while testing it on the remaining subset.
When you are working with large datasets and complex models, the execution of XGBoost and cross-validation can be time-consuming. To speed up the execution, you can use the technique of parallelization. Parallelization is the process of dividing a task into smaller tasks and then executing them simultaneously.
In Python, you can use the library joblib to parallelize the execution of XGBoost and cross-validation. Joblib is a library that provides an easy way to parallelize Python code. It provides a parallel backend for scikit-learn, including XGBoost and cross-validation, so you don’t need to make any changes to your code to use it.
The library provides a function called parallel_backend() which allows you to specify the parallelization method to be used. You can choose from different methods such as threading, multiprocessing or even distributed computing.
You can also specify the number of jobs to be used. This parameter determines how many CPU cores will be used in the parallelization process. It’s important to note that the number of jobs should be equal or less than the number of CPU cores available in your computer.
In summary, parallelization is a technique that can be used to speed up the execution of XGBoost and cross-validation when working with large datasets and complex models. The joblib library in Python provides an easy way to parallelize the execution of XGBoost and cross-validation by providing parallel backends for scikit-learn and also specifying the number of jobs to be used. This can greatly reduce the execution time and make the process more efficient.
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