a MLP model was constructed for the olive oil content data and

ber of hidden neurons was varied to optimise the performance.

25(a) shows the variable importance ranking of the MLP model

vip package. It ranked the fruit weight variable as the most

t one with the positive contribution to oil content as three

ed linear regression models. It also ranked the paste water

as a significantly negative correlated variable. Figure 4.25(b)

e fitness measurements of this MLP model. The R-square was

e F-statistic p value of the model was 1.33e−26. This improved

as due to the use of the MLP, which can explore nonlinearity

e data set.

(a) (b)

a) The vip visualisation of the variable ranking result of the MLP model for

oil content data. The model employs ten hidden neurons. (b) The fitness

nts for the MLP model.

e 4.26(a) shows the ranking result of the SVM models constructed

live oil content data. The ranking was implemented using the

ance package of the rminer package. All variables were given

rankings. In this model, the variable fruit width was ranked the

fruit weight was ranked the third while the paste water was ranked

d. Figure 4.26(b) shows the fitness measurements. The fitness of

M model was also significant. The R-square of the model was

d the F-statistic p value of the model was 6.05e−23.