Question: Suppose we have trained a logistic classifier with( b 0+ b 1 x 1+ b 2 x 2) where the feature vector x =( x

Suppose we have trained a logistic classifier with(b0+b1x1+b2x2) where the feature vectorx=(x1,x2) is two-dimensional andb0=6,b1=0, andb2=1. Suppose we classify data points to be 1 if the estimated probability (from the logistic model) is bigger than 0.5, and 0 otherwise. What's the decision boundary of our classifier?

-Decision boundary is the linex1=6: data points withx1>6 will be classified asY=0 and data points withx16 will be classified asY=1.

-Decision boundary is the linex2=6: data points withx2>6 will be classified asY=1 and data points withx26 will be classified asY=0.

-Decision boundary is the linex2=6: data points withx2>6 will be classified asY=0 and data points withx26 will be classified asY=1.

-Decision boundary is the linex1=6: data points withx1>6 will be classified asY=1 and data points withx16 will be classified asY=0.

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