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I am using GridSearch from sklearn to optimize parameters of the classifier. There is a lot of data, so the whole process of optimization takes a while: more than a day. I would like to watch the performance of the already-tried combinations of parameters during the execution. Is it possible?
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Observing the above time numbers, for parameter grid having 3125 combinations, the Grid Search CV took 10856 seconds (~3 hrs) whereas Halving Grid Search CV took 465 seconds (~8 mins), which is approximate 23x times faster.
GridSearchCV on the other hand, are widely different. Depending on the n_iter chosen, RandomSearchCV can be two, three, four times faster than GridSearchCV. However, the higher the n_iter chosen, the lower will be the speed of RandomSearchCV and the closer the algorithm will be to GridSearchCV.
GridSearchCV tries all the combinations of the values passed in the dictionary and evaluates the model for each combination using the Cross-Validation method. Hence after using this function we get accuracy/loss for every combination of hyperparameters and we can choose the one with the best performance.
Set the verbose parameter in GridSearchCV to a positive number (the greater the number the more detail you will get). For instance:
GridSearchCV(clf, param_grid, cv=cv, scoring='accuracy', verbose=10) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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