The k-Nearest Neighbors (k-NN) algorithm can use various distance metrics to determine the "nearest" neighbors. Here's a brief explanation of each one mentioned:

1. Manhattan: This is a distance metric also known as "Taxicab" or "City Block" distance. It is the sum of the absolute differences of their coordinates. For example, the Manhattan distance between (1,2) and (4,6) is |1-4| + |2-6| = 3 + 4 = 7.

2. Minkowski: This is a generalized metric distance. When used in k-NN, it can behave as Euclidean distance when p=2 or Manhattan distance when p=1.

3. Tanimoto: This is not typically used in k-NN. It's a method for measuring the similarity between two objects, but it's not a distance metric in the traditional sense.

4. Jaccard: Like Tanimoto, this is a similarity measure and is used to compare the similarity and diversity of sample sets. It's not typically used as a distance metric in k-NN.

So, out of the four options, Manhattan and Minkowski can be used as distance metrics in k-NN.

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The k-Nearest Neighbors (k-NN) algorithm can use various distance metrics to determine the "nearest" neighbors. Here's a brief explanation of each one mentioned:

1. Manhattan: This is a distance metric also known as "Taxicab" or "City Block" distance. It is the sum of the absolute differences of their coordinates. For example, the Manhattan distance between (1,2) and (4,6) is |1-4| + |2-6| = 3 + 4 = 7.

2. Minkowski: This is a generalized metric distance. When used in k-NN, it can behave as Euclidean distance when p=2 or Manhattan distance when p=1.

3. Tanimoto: This is not typically used in k-NN. It's a method for measuring the similarity between two objects, but it's not a distance metric in the traditional sense.

4. Jaccard: Like Tanimoto, this is a similarity measure and is used to compare the similarity and diversity of sample sets. It's not typically used as a distance metric in k-NN.

So, out of the four options, Manhattan and Minkowski can be used as distance metrics in k-NN.

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The k-Nearest Neighbors (k-NN) algorithm can use various distance metrics to determine the "nearest" neighbors. Here's a brief explanation of each one mentioned:

1. Manhattan: This is a distance metric also known as "Taxicab" or "City Block" distance. It is the sum of the absolute differences of their coordinates. For example, the Manhattan distance between (1,2) and (4,6) is |1-4| + |2-6| = 3 + 4 = 7.

2. Minkowski: This is a generalized metric distance. When used in k-NN, it can behave as Euclidean distance when p=2 or Manhattan distance when p=1.

3. Tanimoto: This is not typically used in k-NN. It's a method for measuring the similarity between two objects, but it's not a distance metric in the traditional sense.

4. Jaccard: Like Tanimoto, this is a similarity measure and is used to compare the similarity and diversity of sample sets. It's not typically used as a distance metric in k-NN.

So, out of the four options, Manhattan and Minkowski can be used as distance metrics in k-NN.

Which of the following distance metric can be used in k-NN?Review LaterManhattanMinkowskiTanimotoJaccard

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