ppose we have a dataset of eight 3-bit binary vectors and their corresponding labels. Classify a new data point, 010,using a k-nearest neighbor classifier with k=3 and Hamming distance.Data 000 001 010 011 100 101 110 111 111 111point A A B B C C D D D D

Consider a classification problem where the input space consists of 10 binary features (X₁, X₂, ..., X₁₀) and the output space consists of two classes (0 and 1). A dataset of 100 training examples is given, with the following feature values and corresponding class labels:Example 1: (0, 1, 1, 0, 1, 0, 1, 0, 0, 1) → Class: 0Example 2: (1, 0, 0, 1, 1, 1, 0, 1, 0, 1) → Class: 1Example 3: (1, 1, 1, 0, 0, 1, 0, 0, 1, 0) → Class: 1Example 100: (0, 0, 1, 0, 1, 0, 1, 1, 0, 1) → Class: 0You are tasked with building a supervised learning model using logistic regression. Each feature Xi is binary and can take values 0 or 1. You decide to use one-hot encoding to represent the input features. After applying one-hot encoding, how many features will the dataset have for training the logistic regression model?a)19b)20c)10d)18

#<GRADED> def knnclassifier(xTr,yTr,xTe,k): """ function preds=knnclassifier(xTr,yTr,xTe,k); k-nn classifier Input: xTr = nxd input matrix with n row-vectors of dimensionality d xTe = mxd input matrix with m row-vectors of dimensionality d k = number of nearest neighbors to be found Output: preds = 1xd vector of predicted labels, ie preds(i) is the predicted label of xTe(i,:) """ # fix array shapes yTr = yTr.flatten() # Your code goes here raise NotImplementedError('Your code goes here!') return preds #</GRADED> python code with no for loop and return preds

#<GRADED> def knnclassifier(xTr,yTr,xTe,k): """ function preds=knnclassifier(xTr,yTr,xTe,k); k-nn classifier Input: xTr = nxd input matrix with n row-vectors of dimensionality d xTe = mxd input matrix with m row-vectors of dimensionality d k = number of nearest neighbors to be found Output: preds = 1xd vector of predicted labels, ie preds(i) is the predicted label of xTe(i,:) """ # fix array shapes yTr = yTr.flatten() # Your code goes here raise NotImplementedError('Your code goes here!') return preds #</GRADED> use python without using for-loop

(3,7) hamming code represents

<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>