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The simplest case is where the input data can be separated by using "linear decision planes",i.e., linearly separable. Here linear means that the decision surfaces are linear functions of the input array X.
The simplest case is where the input data can be separated by using "linear decision planes",i.e., linearly separable. Here linear means that the decision surfaces are linear functions of the input array X.
Lets think a simple model: a linear function of the input vector with a bias:
Lets think a simple model: a linear function of the input vector with a bias: