Table 1.

Two learning algorithms and their example normal parameters and hyper-parameters.

Learning algorithm	Example hyper-parameters	Example normal parameters
Support vector machine	Regularization constant C, kernel to use, tolerance parameter, ε for round-off error, a polynomial kernel’s degree	Support vectors and their Lagrange multipliers
Random forest	Number of independent variables to examine at each inner node of a classification and regression tree, number of trees	Threshold value and input variable used at each inner node of a tree