Kursinnehåll
Classification with Python
Classification with Python
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.
Swipe to start coding
- Import the
GridSearchCVclass. - Scale the
XusingStandardScaler. - Look for the best value of
n_neighborsamong[3, 9, 18, 27]. - Initialize and train a
GridSearchCVobject with 4 folds of cross-validation. - Print the score of the best model.
Lösning
Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedanTack för dina kommentarer!
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.
Swipe to start coding
- Import the
GridSearchCVclass. - Scale the
XusingStandardScaler. - Look for the best value of
n_neighborsamong[3, 9, 18, 27]. - Initialize and train a
GridSearchCVobject with 4 folds of cross-validation. - Print the score of the best model.
Lösning
Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedanTack för dina kommentarer!
Byt till skrivbordet för praktisk övningFortsätt där du är med ett av alternativen nedan