What is the output of your kNN classifier? Did you obtain a label for each pixel? How is that label computed?

#<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

Which function is used to create a KNN classifier in scikit-learn?Answer areaKNeighborsClassifier()KNeighborsRegressor()KNN()NearestNeighbors()

The confusion matrix highlights a problem of the kNN classifier as it is used now. Can you find it and explain why?

Given a 2-class dataset as shown in the picture below (red - positive class and blue - negative class), we apply kNN to perform the classification. Every time we take one sample as the testing point (i.e. the new point) and use the other 8 samples to classify this point; this is repeated 9 times, so each sample becomes the testing point once; based on the above, we can obtain an accuracy (or error rate).  Which of the following value of K will produce the lowest error?Group of answer choicesk = 3k = 2k = 1k = 5

In KNN, how is the class of a new data point determined in classification tasks?*1 pointMajority vote of its K nearest neighborsAverage of its K nearest neighbors' valuesWeighted vote of its K nearest neighborsRandom selection from its K nearest neighbors