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Вивчайте Challenge: Choosing the Best K Value. | k-NN Classifier
Classification with Python

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Challenge: Choosing the Best K Value.

As shown in the previous chapters, the model makes different predictions for different k(neighbors number) values.
When we build a model, we want to choose the k that will lead to the best performance. And in the previous chapter, we learned how to measure performance using cross-validation.
Running a loop and calculating cross-validation scores for some range of k values to choose the highest sounds like a no-brainer. And that's the most frequently used approach. sklearn has a neat class for that task.

The param_grid parameter takes a dictionary with parameter names as keys and a list of items to go through as a list. For example, to try values 1-99 for n_neighbors, you would use:

python

The .fit(X, y) method leads the GridSearchCV object to find the best parameters from param_grid and re-train the model with the best parameters using the whole set.
You can then get the highest score using the .best_score_ attribute and predict new values using the .predict() method.

Завдання

Swipe to start coding

  1. Import the GridSearchCV class.
  2. Scale the X using StandardScaler.
  3. Look for the best value of n_neighbors among [3, 9, 18, 27].
  4. Initialize and train a GridSearchCV object with 4 folds of cross-validation.
  5. Print the score of the best model.

Рішення

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book
Challenge: Choosing the Best K Value.

As shown in the previous chapters, the model makes different predictions for different k(neighbors number) values.
When we build a model, we want to choose the k that will lead to the best performance. And in the previous chapter, we learned how to measure performance using cross-validation.
Running a loop and calculating cross-validation scores for some range of k values to choose the highest sounds like a no-brainer. And that's the most frequently used approach. sklearn has a neat class for that task.

The param_grid parameter takes a dictionary with parameter names as keys and a list of items to go through as a list. For example, to try values 1-99 for n_neighbors, you would use:

python

The .fit(X, y) method leads the GridSearchCV object to find the best parameters from param_grid and re-train the model with the best parameters using the whole set.
You can then get the highest score using the .best_score_ attribute and predict new values using the .predict() method.

Завдання

Swipe to start coding

  1. Import the GridSearchCV class.
  2. Scale the X using StandardScaler.
  3. Look for the best value of n_neighbors among [3, 9, 18, 27].
  4. Initialize and train a GridSearchCV object with 4 folds of cross-validation.
  5. Print the score of the best model.

Рішення

Switch to desktopПерейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів
Все було зрозуміло?

Як ми можемо покращити це?

Дякуємо за ваш відгук!

close

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Completion rate improved to 3.57

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