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!

This exercise is part of the course

Ensemble Methods in Python

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Exercise 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.

Hands-on interactive exercise

Have a go at this exercise by completing this sample 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)))

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