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Data Science Interview Challenge
Data Science Interview Challenge
Challenge 5: Hyperparameter Tuning
Hyperparameter tuning involves adjusting the parameters of an algorithm to optimize its performance. Unlike model parameters, which the algorithm learns on its own during training, hyperparameters are external configurations preset before the learning process begins. The primary purpose of hyperparameter tuning is to find the optimal combination of hyperparameters that minimizes a predefined loss function or maximizes accuracy, ensuring that the model neither underfits nor overfits the data.
Swipe to show code editor
Perform hyperparameter tuning on a RandomForest classifier to predict wine types based on their chemical properties using GridSearchCV and RandomizedSearchCV.
- Define a parameter grid to search through. The number of trees should be iterating over the list
[10, 20, 30], and the maximum depth of them should be iterating over[5, 10, 20]. - Use
GridSearchCVto find the best hyperparameters for the RandomForest classifier with3folds of data. - Do the same for
RandomizedSearchCVfor5random sets of parameters. - Compare the results of both search methods.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Challenge 5: Hyperparameter Tuning
Hyperparameter tuning involves adjusting the parameters of an algorithm to optimize its performance. Unlike model parameters, which the algorithm learns on its own during training, hyperparameters are external configurations preset before the learning process begins. The primary purpose of hyperparameter tuning is to find the optimal combination of hyperparameters that minimizes a predefined loss function or maximizes accuracy, ensuring that the model neither underfits nor overfits the data.
Swipe to show code editor
Perform hyperparameter tuning on a RandomForest classifier to predict wine types based on their chemical properties using GridSearchCV and RandomizedSearchCV.
- Define a parameter grid to search through. The number of trees should be iterating over the list
[10, 20, 30], and the maximum depth of them should be iterating over[5, 10, 20]. - Use
GridSearchCVto find the best hyperparameters for the RandomForest classifier with3folds of data. - Do the same for
RandomizedSearchCVfor5random sets of parameters. - Compare the results of both search methods.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Challenge 5: Hyperparameter Tuning
Hyperparameter tuning involves adjusting the parameters of an algorithm to optimize its performance. Unlike model parameters, which the algorithm learns on its own during training, hyperparameters are external configurations preset before the learning process begins. The primary purpose of hyperparameter tuning is to find the optimal combination of hyperparameters that minimizes a predefined loss function or maximizes accuracy, ensuring that the model neither underfits nor overfits the data.
Swipe to show code editor
Perform hyperparameter tuning on a RandomForest classifier to predict wine types based on their chemical properties using GridSearchCV and RandomizedSearchCV.
- Define a parameter grid to search through. The number of trees should be iterating over the list
[10, 20, 30], and the maximum depth of them should be iterating over[5, 10, 20]. - Use
GridSearchCVto find the best hyperparameters for the RandomForest classifier with3folds of data. - Do the same for
RandomizedSearchCVfor5random sets of parameters. - Compare the results of both search methods.
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Hyperparameter tuning involves adjusting the parameters of an algorithm to optimize its performance. Unlike model parameters, which the algorithm learns on its own during training, hyperparameters are external configurations preset before the learning process begins. The primary purpose of hyperparameter tuning is to find the optimal combination of hyperparameters that minimizes a predefined loss function or maximizes accuracy, ensuring that the model neither underfits nor overfits the data.
Swipe to show code editor
Perform hyperparameter tuning on a RandomForest classifier to predict wine types based on their chemical properties using GridSearchCV and RandomizedSearchCV.
- Define a parameter grid to search through. The number of trees should be iterating over the list
[10, 20, 30], and the maximum depth of them should be iterating over[5, 10, 20]. - Use
GridSearchCVto find the best hyperparameters for the RandomForest classifier with3folds of data. - Do the same for
RandomizedSearchCVfor5random sets of parameters. - Compare the results of both search methods.
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