The Data Science
 Workshop by Anthony So Thomas V. Joseph Robert Thas John 
Andrew Worsley and Dr. Samuel Asare

Author:Anthony So, Thomas V. Joseph, Robert Thas John,
Andrew Worsley, and Dr. Samuel Asare
Language: eng
Format: epub
Publisher: Packt Publishing Pvt Ltd
Published: 2020-01-28T00:00:00+00:00

# and every possible weight_function in the grid

for weight_function in grid['weight_function']:

# initialize the knn estimator

knn = neighbors.KNeighborsClassifier(n_neighbors=k, weights=weight_function)

# conduct a 10-fold cross-validation

cv = model_selection.cross_val_score(knn, X, y, cv=10, scoring='precision')

# calculate the average precision value over all folds

cv_mean = round(cv.mean(), 3)

# report the result

print('With k = {} and weight function = {}, mean precision = {}'.format(k, weight_function, cv_mean))

The output will be as follows:

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