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ML Introduction with scikit-learn
ML Introduction with scikit-learn
Tune Hyperparameters with RandomizedSearchCV
The idea behind RandomizedSearchCV is that it works the same as GridSearchCV, but instead of trying all the combinations, it tries a randomly sampled subset.
For example, this param_grid will have 100 combinations
The GridSearchCV would try all of them, which is time-consuming.
With RandomizedSearchCV, you can try only a randomly chosen subset of, say, 20 combinations.
It usually leads to a little worse result, but it is much faster.
You can control the number of combinations to be tested using the n_iter argument (set to 10 by default). Apart from that, working with it is the same as with GridSearchCV.
Завдання
Your task is to build GridSearchCV and RandomizedSearchCV with 20 combinations and compare the results.
- Initialize the
RandomizedSearchCVobject. Pass the parameters grid and set the number of combinations to 20. - Initialize the
GridSearchCVobject. - Train both
GridSearchCVandRandomizedSearchCVobjects. - Print the best estimator of
grid. - Print the best score of
randomized.
Note
You can try running the code several times. Look at the difference between the two scores. Sometimes the scores can be the same due to the presence of the best parameters among combinations sampled by
RandomizedSearchCV.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Tune Hyperparameters with RandomizedSearchCV
The idea behind RandomizedSearchCV is that it works the same as GridSearchCV, but instead of trying all the combinations, it tries a randomly sampled subset.
For example, this param_grid will have 100 combinations
The GridSearchCV would try all of them, which is time-consuming.
With RandomizedSearchCV, you can try only a randomly chosen subset of, say, 20 combinations.
It usually leads to a little worse result, but it is much faster.
You can control the number of combinations to be tested using the n_iter argument (set to 10 by default). Apart from that, working with it is the same as with GridSearchCV.
Завдання
Your task is to build GridSearchCV and RandomizedSearchCV with 20 combinations and compare the results.
- Initialize the
RandomizedSearchCVobject. Pass the parameters grid and set the number of combinations to 20. - Initialize the
GridSearchCVobject. - Train both
GridSearchCVandRandomizedSearchCVobjects. - Print the best estimator of
grid. - Print the best score of
randomized.
Note
You can try running the code several times. Look at the difference between the two scores. Sometimes the scores can be the same due to the presence of the best parameters among combinations sampled by
RandomizedSearchCV.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Tune Hyperparameters with RandomizedSearchCV
The idea behind RandomizedSearchCV is that it works the same as GridSearchCV, but instead of trying all the combinations, it tries a randomly sampled subset.
For example, this param_grid will have 100 combinations
The GridSearchCV would try all of them, which is time-consuming.
With RandomizedSearchCV, you can try only a randomly chosen subset of, say, 20 combinations.
It usually leads to a little worse result, but it is much faster.
You can control the number of combinations to be tested using the n_iter argument (set to 10 by default). Apart from that, working with it is the same as with GridSearchCV.
Завдання
Your task is to build GridSearchCV and RandomizedSearchCV with 20 combinations and compare the results.
- Initialize the
RandomizedSearchCVobject. Pass the parameters grid and set the number of combinations to 20. - Initialize the
GridSearchCVobject. - Train both
GridSearchCVandRandomizedSearchCVobjects. - Print the best estimator of
grid. - Print the best score of
randomized.
Note
You can try running the code several times. Look at the difference between the two scores. Sometimes the scores can be the same due to the presence of the best parameters among combinations sampled by
RandomizedSearchCV.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
The idea behind RandomizedSearchCV is that it works the same as GridSearchCV, but instead of trying all the combinations, it tries a randomly sampled subset.
For example, this param_grid will have 100 combinations
The GridSearchCV would try all of them, which is time-consuming.
With RandomizedSearchCV, you can try only a randomly chosen subset of, say, 20 combinations.
It usually leads to a little worse result, but it is much faster.
You can control the number of combinations to be tested using the n_iter argument (set to 10 by default). Apart from that, working with it is the same as with GridSearchCV.
Завдання
Your task is to build GridSearchCV and RandomizedSearchCV with 20 combinations and compare the results.
- Initialize the
RandomizedSearchCVobject. Pass the parameters grid and set the number of combinations to 20. - Initialize the
GridSearchCVobject. - Train both
GridSearchCVandRandomizedSearchCVobjects. - Print the best estimator of
grid. - Print the best score of
randomized.
Note
You can try running the code several times. Look at the difference between the two scores. Sometimes the scores can be the same due to the presence of the best parameters among combinations sampled by
RandomizedSearchCV.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів