K-nearest neighbors for mushrooms

The Gaussian Naive Bayes classifier did a really good job for being an initial model. Let's now build a new model to compare it against the Naive Bayes.

In this case, the algorithm to use is a 5-nearest neighbors classifier. As the dummy features create a high-dimensional dataset, use the Ball Tree algorithm to make the model faster. Let's see how this model performs!

Cet exercice fait partie du cours

Ensemble Methods in Python

Afficher le cours

Instructions

Build a KNeighborsClassifier with 5 neighbors and algorithm = 'ball_tree' (to expedite the processing).
Fit the model to the training data.
Evaluate the performance on the test set using the accuracy score.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Instantiate a 5-nearest neighbors classifier with 'ball_tree' algorithm
clf_knn = ____(____, ____)

# Fit the model to the training set
____

# Calculate the predictions on the test set
pred = ____

# Evaluate the performance using the accuracy score
print("Accuracy: {:0.4f}".format(accuracy_score(y_test, pred)))

Modifier et exécuter le code