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GO Classes DPP | ML | PERCEPTRON

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The decision boundary will be perpendicular to $w$.

If the weight vector is multiplied by -1 , then for the data points on the RHS, $w^T x$ will be less than 0, and on the LHS, it will be greater than or equal to zero.

Accordingly, LHS data points (and hence $x_1$) will be predicted as +1 .

And RHS data points (and hence $x_2$) will be predicted as -1 .
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