Зміст курсу
Classification with Python
Classification with Python
Challenge: Choosing the Best K Value
As demonstrated in the previous chapters, the model makes different predictions based on the k (number of neighbors) values. When building a model, it's crucial to select the k value that yields the best performance.
You can use cross-validation to measure the model's performance. Running a loop and calculating cross-validation scores for a range of k values to select the highest is a straightforward approach. This is the most commonly used method. sklearn provides a convenient GridSearchCV class for this 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.
Swipe to start coding
You are given the Star Wars ratings dataset stored as a DataFrame in the df variable.
- Initialize
param_gridas a dictionary containing then_neighborsparameter with the values[3, 9, 18, 27]. - Create a
GridSearchCVobject usingparam_gridwith 4-fold cross-validation, train it, and store it in thegrid_searchvariable. - Retrieve the best model from
grid_searchand store it in thebest_modelvariable. - Retrieve the score of the best model and store it in the
best_scorevariable.
Рішення
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Challenge: Choosing the Best K Value
As demonstrated in the previous chapters, the model makes different predictions based on the k (number of neighbors) values. When building a model, it's crucial to select the k value that yields the best performance.
You can use cross-validation to measure the model's performance. Running a loop and calculating cross-validation scores for a range of k values to select the highest is a straightforward approach. This is the most commonly used method. sklearn provides a convenient GridSearchCV class for this 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.
Swipe to start coding
You are given the Star Wars ratings dataset stored as a DataFrame in the df variable.
- Initialize
param_gridas a dictionary containing then_neighborsparameter with the values[3, 9, 18, 27]. - Create a
GridSearchCVobject usingparam_gridwith 4-fold cross-validation, train it, and store it in thegrid_searchvariable. - Retrieve the best model from
grid_searchand store it in thebest_modelvariable. - Retrieve the score of the best model and store it in the
best_scorevariable.
Рішення
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантівДякуємо за ваш відгук!
Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів