Another training data point, xm, is the 2nd nearest neighbor of x. Show the conditionalprobability of misclassification error considering both xn and xm, P (e|x, xn, xm).

Probability of Error: For performing classification, Bayesian selection criteria minimizes theprobability of misclassification. When classifying an input x, the Bayesian selection criteriawill assign x to its most probable class. Given a set of L classes (c1, c2, ..., cL), the probability ofx belonging to class ci is given as P (ci|x). The maximum conditional probability is describedas: P (ci∗ |x) = argmaxiP (ci|x). From this, derive the Bayesian conditional probability ofmisclassification, P ∗(e|x), for a given input x and express its average over the prior distributionof x.

k-NN: This problem derives a performance bound on the 1-NN classifier. Consider the 1-NN binaryclassifier on a two-dimensional space. For an input x and its nearest neighbor xn in the trainingdata, the conditional probabilities of belonging to classes c1 and c2 are as follows:◦ p(c1|x) : probability that x belongs to c1◦ p(c2|x) : probability that x belongs to c2◦ p(c1|xn) : probability that xn belongs to c1◦ p(c2|xn) : probability that xn belongs to c2Knowing that p(c1|x) + p(c2|x) = 1 and p(c1|xn) + p(c2|xn) = 1,(a) Express the conditional probability of misclassification error, e, for x, P (e|x, xn), in terms ofthe probabilities provided above.

Find more data and re-train your classifier to classify the misclassified sample, show your new classifier works on the miss classified image

Consider a binary classification problem with two classes, A and B with prior probability P(A)=0.6𝑃(𝐴)=0.6 and P(B)=0.4𝑃(𝐵)=0.4 .Let X be a single binary feature that can take values 00 or 11. Given: P(X=1|A)=0.8𝑃(𝑋=1|𝐴)=0.8 and P(X=0|B)=0.7𝑃(𝑋=0|𝐵)=0.7. Determine which class the classifier will classify when X=1𝑋=1.

Support Vector Machine is-(1 Point)a discriminative classifiera lazy learning classifiera probabilistic classifierNone