What are the distance measures that can be used with KNN?

Specifically, four different distance functions, which are Euclidean distance, cosine similarity measure, Minkowsky, correlation, and Chi square, are used in the k-NN classifier respectively.

Which distance measure is mostly used for numeric attributes in KNN classifier?

ED is the most widely used distance metric in KNN classifications; however, only few studies examined the effect of different distance metrics on the performance of KNN, these used a small number of distances, a small number of data sets, or both.

Can Hamming distance be used in KNN?

Hamming Distance: It is used for categorical variables. If the value (x) and the value (y) are the same, the distance D will be equal to 0 . Otherwise D=1.

How do you evaluate a KNN model?

Evaluation procedure 1 – Train and test on the entire dataset. Train the model on the entire dataset. Test the model on the same dataset, and evaluate how well we did by comparing the predicted response values with the true response values.

How do you calculate Euclidean distance from KNN?

The formula to calculate Euclidean distance is: For each dimension, we subtract one point’s value from the other’s to get the length of that “side” of the triangle in that dimension, square it, and add it to our running total. The square root of that running total is our Euclidean distance.

Which of the following distance measure do we use in case of categorical variables in KNN?

Hamming distance
In the case of categorical variables you must use the Hamming distance, which is a measure of the number of instances in which corresponding symbols are different in two strings of equal length. Choosing the optimal value for K is best done by first inspecting the data.

How do you use Manhattan distance in KNN?

The formula to calculate Manhattan distance is: The left side of the equals sign just means “the distance between point x and point y”. The ∑ just means “the cumulative sum of each step”. So far, this discussion has explored Manhattan distance in a 2-dimensional space.

How do you find the minimum distance in KNN?

Here is step by step on how to compute K-nearest neighbors KNN algorithm:

1. Determine parameter K = number of nearest neighbors.
2. Calculate the distance between the query-instance and all the training samples.
3. Sort the distance and determine nearest neighbors based on the K-th minimum distance.

How accurate is KNN?

The result of their research revealed that NCC reach a highest accuracy of 96.67\% and a lowest accuracy of 33.33\%, whereas the kNN method was only capable to produce a highest accuracy of 26.7\% and a lowest accuracy of 22.5\%.