We have studied a linear classifier Logistic Regression y = (wx+wo). If z >= 0, (z)...

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We have studied a linear classifier Logistic Regression y = (wx+wo). If z >= 0, (z) >= 0.5 and y can be regarded as class '1', and otherwise '0' if z < 0. The parameters to be learnt are w and wo. The "Nearest neighbour classifier" (NN) is a different approach to learning from data. Suppose we are given N points (x1,y1),..., (XN, YN) where y = {0, 1}; for a parameter k and given a new point x*, the k-NN approach does the following: find xj, j the k-closest points to x*, then output y* as the majority label from the set {Yj, Yjk}, i.e., the most commonly occurring label among the k-nearest neighbours. 4. What is the computational cost of predicting the label * ? We have studied a linear classifier Logistic Regression y = (wx+wo). If z >= 0, (z) >= 0.5 and y can be regarded as class '1', and otherwise '0' if z < 0. The parameters to be learnt are w and wo. The "Nearest neighbour classifier" (NN) is a different approach to learning from data. Suppose we are given N points (x1,y1),..., (XN, YN) where y = {0, 1}; for a parameter k and given a new point x*, the k-NN approach does the following: find xj, j the k-closest points to x*, then output y* as the majority label from the set {Yj, Yjk}, i.e., the most commonly occurring label among the k-nearest neighbours. 4. What is the computational cost of predicting the label * ?