Ridge parameter

FIGURE4.38 Ridge regression for the PAC data set. The optimal ridge parameter AK —4.3 is evaluated using repeated 10-fold CV. The resulting (average) predictions (in black) versus the measured y-values are shown with two different scales because of severe prediction errors for two objects. 

The parameter RP among the input data is the ridge parameter that will be exploited in Section 3.5. In normal regression problems RP = 0 should be used. 

RIDGE PARAMETER HERO FOR ORDINARY LEAST SQUARES)... 

Fig. 3.2. Relative change of the estimates as a function of the ridge parameter...

The regression coefEcients are biased by introducing a parameter along the diameter of Z Z [109]. The computation of regression coefficients /3 in Eq. 4.3 is modified by introducing a ridge parameter k ... 

Unfortunately, before RR can be applied, a value of the ridge parameter d must be fovmd. One method that is relatively simple to apply is to perform RR for a range of values of in the interval (0, 1) and to then select as the... 

In addition to the set of adjustable coefficients and b contained in Eq. (13.7), the method CMF also requires ealeulation of a certain nmnber of adjustable parameters. Among them are the parameter v for the support vector regression method v-S VR and the ridge parameter y for KRR. Their values should be optimized with the aim to improve the predictive eapability of the model constracted. In addition, for each molecular field one can adjust the values of up to two parameters (attenuation factor, which is related to the width of the Gaussian function) and hj (mixing coefficient, which has the meaning of the relative contribution of molecular field of the ythtype). 

---