Identify the difficulties with the k-nearest neighbor algorithm.(1 Point)Curse of dimensionalityCalculate the distance of the test case from all training casesBoth A and BNone of the above

Identify the difficulties with the k-nearest neighbor algorithm.

Which one of the following statements is true regarding K Nearest Neighbors?1 pointFor high dimensional data, the best distance measure to use for KNN is the Euclidean distance.The Manhattan distance between two data points is the square root of the sum of the squares of the differences between the individual feature values of the data points.The distance between two data points is independent of the scale of their features.K Nearest Neighbors (KNN) assumes that points which are close together are similar.

What is the main goal of the k-nearest neighbors (k-NN) algorithm in data classification?To perform dimensionality reduction on the datasetTo classify data points based on the majority class among their k nearest neighborsTo generate association rules from transactional dataTo find the optimal number of clusters in the dataClear selection

<p>(b) Implement the function <b><code>findknn</code></b>, which should find the $k$ nearest neighbors of a set of vectors within a given training data set. Break ties arbitrarily. The call of <pre> [I,D]=findknn(xTr,xTe,k); </pre> should result in two matrices $I$ and $D$, both of dimensions $k\times m$, where $m$ is the number of input vectors in <code>xTe</code>. The matrix $I(i,j)$ is the index of the $i^{th}$ nearest neighbor of the vector $xTe(j,:)$. So, for example, if we set <code>i=I(1,3)</code>, then <code>xTr(i,:)</code> is the first nearest neighbor of vector <code>xTe(3,:)</code>. The second matrix $D$ returns the corresponding distances. So $D(i,j)$ is the distance of $xTe(j,:)$ to its $i^{th}$ nearest neighbor. </p>

Question 3Which one of the following statements is most accurate about K Nearest Neighbors (KNN)? 1 pointKNN is a classification model.KNN is an unsupervised learning method. KNN is a regression model.KNN can be used for both classification and regression.