Cursusinhoud
Classification with Python
Classification with Python
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.
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.
Oplossing
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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.
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.
Oplossing
Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande optiesBedankt voor je feedback!
Schakel over naar desktop voor praktijkervaringGa verder vanaf waar je bent met een van de onderstaande opties