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Challenge: Choosing the Best K Value

As shown in the previous chapters, the model's predictions can vary depending on the value of k (the number of neighbors). When building a k-NN model, it's important to choose the k value that gives the best performance.

A common approach is to use cross-validation to evaluate model performance. You can run a loop and calculate cross-validation scores for a range of k values, then select the one with the highest score. This is the most widely used method.

To perform this, sklearn offers a convenient tool: the GridSearchCV class.

The param_grid parameter takes a dictionary where the keys are parameter names and the values are lists of options to try. For example, to test values from 1 to 99 for n_neighbors, you can write:


python

Calling the .fit(X, y) method on the GridSearchCV object will search through the parameter grid to find the best parameters and then re-train the model on the entire dataset using those best parameters.

You can access the best score using the .best_score_ attribute and make predictions with the optimized model using the .predict() method. Similarly, you can retrieve the best model itself using the .best_estimator_ attribute.

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You are given the Star Wars ratings dataset stored as a DataFrame in the df variable.

Initialize param_grid as a dictionary containing the n_neighbors parameter with the values [3, 9, 18, 27].
Create a GridSearchCV object using param_grid with 4-fold cross-validation, train it, and store it in the grid_search variable.
Retrieve the best model from grid_search and store it in the best_model variable.
Retrieve the score of the best model and store it in the best_score variable.

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Challenge: Choosing the Best K Value

To perform this, sklearn offers a convenient tool: the GridSearchCV class.


python

Opgave

Swipe to start coding

You are given the Star Wars ratings dataset stored as a DataFrame in the df variable.

Initialize param_grid as a dictionary containing the n_neighbors parameter with the values [3, 9, 18, 27].
Create a GridSearchCV object using param_grid with 4-fold cross-validation, train it, and store it in the grid_search variable.
Retrieve the best model from grid_search and store it in the best_model variable.
Retrieve the score of the best model and store it in the best_score variable.

Løsning

Skift til skrivebord for at øve i den virkelige verdenFortsæt der, hvor du er, med en af nedenstående muligheder

Var alt klart?

Tak for dine kommentarer!

Stryg for at vise menuen

Challenge: Choosing the Best K Value

Løsning

Awesome!

Challenge: Choosing the Best K Value

Løsning

Awesome!