GridSearchCV in Scikit- Learn is a useful tool for hyperparameter tuning in machine learning models, but it can be computationally expensive for large datasets and complex models. One way to speed up the process is to use parallel processing.
Here are some ways to parallelize GridSearchCV using parallel processing in Scikit-Learn:
1. n_jobs parameter:
Scikit-learn’s GridSearchCV has a built-in parallelization feature that can be enabled by setting the “n_jobs” parameter to the number of cores available on your machine. For example:
In this example, we set n_jobs=-1 to use all available cores.
- This code performs a grid search cross-validation on a random forest classifier.
- It generates a synthetic dataset using the make_classification function.
- The parameter grid defines hyperparameters with different values for the number of trees and maximum depth.
- The best hyperparameters and the corresponding score are printed.
2. Dask-ml
Dask-ml is a library that provides distributed machine learning algorithms for large datasets. It has a GridSearchCV function that is similar to scikit-learn’s GridSearchCV , but it supports parallel processing with Dask. Here’s an example:
- This code performs a grid search cross-validation on a random forest classifier.
- It generates a synthetic dataset using the make_classification function.
- The parameter grid defines hyperparameters with different values for number of trees and maximum depth.
- The best hyperparameters and the corresponding score are printed.
- The model is trained using the best hyperparameters.
- The trained model is used to predict the first five samples of the dataset.