Machine Learning, Logistic Regression, Non Linear.
is fine because sin(-k) = -sin(k). Table 2. Coefficients obtained for the generic case 5. Conclusions Logistic regression has traditionally been used to come up with a hyperplane that separates the feature space into classes. But if we suspect that the decision boundary is nonlinear we may get