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Challenge 5: Hyperparameter Tuning | Scikit-learn
Data Science Interview Challenge
course content

Course Content

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
2. NumPy
3. Pandas
4. Matplotlib
5. Seaborn
6. Statistics
7. Scikit-learn

bookChallenge 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.

Task
test

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.

  1. 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].
  2. Use GridSearchCV to find the best hyperparameters for the RandomForest classifier with 3 folds of data.
  3. Do the same for RandomizedSearchCV for 5 random sets of parameters.
  4. Compare the results of both search methods.

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Section 7. Chapter 5
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bookChallenge 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.

Task
test

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.

  1. 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].
  2. Use GridSearchCV to find the best hyperparameters for the RandomForest classifier with 3 folds of data.
  3. Do the same for RandomizedSearchCV for 5 random sets of parameters.
  4. Compare the results of both search methods.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 7. Chapter 5
toggle bottom row

bookChallenge 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.

Task
test

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.

  1. 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].
  2. Use GridSearchCV to find the best hyperparameters for the RandomForest classifier with 3 folds of data.
  3. Do the same for RandomizedSearchCV for 5 random sets of parameters.
  4. Compare the results of both search methods.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

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.

Task
test

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.

  1. 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].
  2. Use GridSearchCV to find the best hyperparameters for the RandomForest classifier with 3 folds of data.
  3. Do the same for RandomizedSearchCV for 5 random sets of parameters.
  4. Compare the results of both search methods.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 7. Chapter 5
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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