Contenido del Curso
ML Introduction with scikit-learn
ML Introduction with scikit-learn
Challenge: Tuning 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 works 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.
Swipe to show code editor
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.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Challenge: Tuning 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 works 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.
Swipe to show code editor
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.
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones¡Gracias por tus comentarios!
Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones