Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Challenge 5: Hyperparameter Tuning | Scikit-learn
Data Science Interview Challenge
course content

Зміст курсу

Data Science Interview Challenge

Data Science Interview Challenge

1. Python
IntroductionChallenge 1: List ComprehensionChallenge 2: String ManipulationChallenge 3: Dictionary ManipulationChallenge 4: Nested LoopsChallenge 5: ClassesTheoretical Questions
2. NumPy
Challenge 1: Array CreationChallenge 2: Array ManipulationChallenge 3: Statistical InsightsChallenge 4: Handling Missing ValuesChallenge 5: Subarray SortingTheoretical Questions
3. Pandas
Challenge 1: DataFrame CreationChallenge 2: Data GroupingChallenge 3: Indexing and MultiIndexingChallenge 4: Altering DataFrameChallenge 5: Iterating Over DataTheoretical Questions
4. Matplotlib
Challenge 1: Fundamentals of PlottingChallenge 2: Other Graph TypesChallenge 3: SubplotsChallenge 4: Customizing PlotsChallenge 5: Adding Text to PlotsTheoretical Questions
5. Seaborn
Challenge 1: Visualizing DistributionsChallenge 2: Exploring Categorical DataChallenge 3: Relational PlotsChallenge 4: Regression PlotsChallenge 5: Matrix PlotsTheoretical Questions
6. Statistics
Challenge 1: Probabilities and DistributionsChallenge 2: Bayes' TheoremChallenge 3: Hypothesis TestingChallenge 4: Confidence IntervalsChallenge 5: CorrelationTheoretical Questions
7. Scikit-learn
Challenge 1: Data ScalingChallenge 2: Basic Model CreationChallenge 3: PipelinesChallenge 4: Cross-validationChallenge 5: Hyperparameter TuningTheoretical Questions

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.

Завдання

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.

Завдання

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.

Все було зрозуміло?

Секція 7. Розділ 5
toggle bottom row

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.

Завдання

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.

Завдання

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.

Все було зрозуміло?

Секція 7. Розділ 5
toggle bottom row

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.

Завдання

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.

Завдання

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.

Все було зрозуміло?

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.

Завдання

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.

Секція 7. Розділ 5
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
We're sorry to hear that something went wrong. What happened?
some-alt