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Which is the best k nearest neighbor classifier?
The k-nearest neighbor classifier fundamentally relies on a distance metric. The better that metric reflects label similarity, the better the classified will be. The most common choice is the Minkowski distance dist (x, z) = (∑ r = 1 d | x r − z r | p) 1 / p.
What is the Hart algorithm for k-NN classification?
Condensed nearest neighbor (CNN, the Hart algorithm) is an algorithm designed to reduce the data set for k-NN classification. It selects the set of prototypes U from the training data, such that 1NN with U can classify the examples almost as accurately as 1NN does with the whole data set.
What is the output of k-NN regression?
If k = 1, then the object is simply assigned to the class of that single nearest neighbor. In k-NN regression, the output is the property value for the object. This value is the average of the values of k nearest neighbors.
Which is nearest neighbour classifier guarantees the worst error rate?
As the size of training data set approaches infinity, the one nearest neighbour classifier guarantees an error rate of no worse than twice the Bayes error rate (the minimum achievable error rate given the distribution of the data). and all others 0 weight. This can be generalised to weighted nearest neighbour classifiers.
How is the weight of the k nearest neighbors multiplied?
The class (or value, in regression problems) of each of the k nearest points is multiplied by a weight proportional to the inverse of the distance from that point to the test point. Another way to overcome skew is by abstraction in data representation.
When to use KNN for regression and classification?
KNN can be used for regression and classification problems. When KNN is used for regression problems the prediction is based on the mean or the median of the K-most similar instances. When KNN is used for classification, the output can be calculated as the class with the highest frequency from the K-most similar instances.