## Which distance metric can be used in KNN?

Euclidean distance 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 datasets, or both.

**Is Manhattan distance used in KNN?**

Manhattan distance is a good measure to use if the input variables are not similar in type (such as age, gender, height, etc.). The value for K can be found by algorithm tuning. It is a good idea to try many different values for K (e.g. values from 1 to 21) and see what works best for your problem.

**What kind of distance metrics are suitable for categorical variables to find the closest neighbors?**

Both Euclidean and Manhattan distances are used in case of continuous variables, whereas hamming distance is used in case of categorical variable.

### How do you find the distance between K nearest neighbors?

Calculating distance:

- Get each characteristic from your dataset;
- Subtract each one, example, (line 1, column 5) — (line1,column5) = X … (line 1, column 13) — (line1,column13) = Z;
- After get the subtract of all columns, you will get all the results and sum it X+Y +Z… ;
- So you wil get the sum’s square root ;

**What kind of distance metrics are suitable for categorical variables to find the closest neighbor?**

**What is Manhattan distance used for?**

Manhattan Distance: We use Manhattan distance, also known as city block distance, or taxicab geometry if we need to calculate the distance between two data points in a grid-like path.

## Why do we use Euclidean distance in KNN?

Euclidean Distance We mostly use this distance measurement technique to find the distance between consecutive points. It is generally used to find the distance between two real-valued vectors. Euclidean distance is used when we have to calculate the distance of real values like integer, float, etc…

**How do you determine the most ideal k size in K-nearest neighbor?**

The optimal K value usually found is the square root of N, where N is the total number of samples. Use an error plot or accuracy plot to find the most favorable K value. KNN performs well with multi-label classes, but you must be aware of the outliers.

**What sort of data does K nearest neighbors classify best on?**

It can be used for both classification and regression problems. It’s ideal for non-linear data since there’s no assumption about underlying data. It can naturally handle multi-class cases.

### Is Manhattan distance better than Euclidean?

Thus, Manhattan Distance is preferred over the Euclidean distance metric as the dimension of the data increases. This occurs due to something known as the ‘curse of dimensionality’.

**What kind of distance metrics are suitable for categorical variables to find the closest Neighbour?**

**Why is choosing a large value of k for KNN classification algorithm beneficial?**

In KNN, finding the value of k is not easy. A small value of k means that noise will have a higher influence on the result and a large value make it computationally expensive. Data scientists usually choose as an odd number if the number of classes is 2 and another simple approach to select k is set k=sqrt(n).

## Why the KNN algorithm Cannot be used for large datasets?

6. Why should we not use KNN algorithm for large datasets? KNN works well with smaller dataset because it is a lazy learner. It needs to store all the data and then makes decision only at run time.

**Why is Euclidean distance better than Manhattan?**

While Euclidean distance gives the shortest or minimum distance between two points, Manhattan has specific implementations. For example, if we were to use a Chess dataset, the use of Manhattan distance is more appropriate than Euclidean distance.

**Which distance metric is best?**

This means that the L1 distance metric (Manhattan Distance metric) is the most preferable for high dimensional applications.” Thus, Manhattan Distance is preferred over the Euclidean distance metric as the dimension of the data increases. This occurs due to something known as the ‘curse of dimensionality’.

### What kind of distance metric is suitable for categorical variables to find the closest Neighbour?

hamming distance

Both Euclidean and Manhattan distances are used in case of continuous variables, whereas hamming distance is used in case of categorical variable.

**What happens when the K value is too large?**

If K is a large number, it means that the equilibrium concentration of the products is large. In this case, the reaction as written will proceed to the right (resulting in an increase in the concentration of products)