Projection Pursuit Regression Github. Both projection pursuit regression and neural networks models project the input vector onto a one-dimensional hyperplane and then applies a nonlinear transformation of the input variables that are then added in a linear fashion. The projection pursuit paradigm has been applied to density estimation 6 8 12 13 regression 7 and classification 5.
Projection pursuit regression 16 24 15 26 is widely-used for high-dimensional nonparametric regression models which takes the form. The ppdire package provides a framework to calculate PP estimates based on a wide variety of projection indices. Rob J Hyndman 2006 Another look at measures of forecast accuracy for intermittent demand.
See datasetsmtcars for details data mtcars Fit a projection pursuit regression model fit -lm mpg data mtcars Compute approximate Shapley values using 10 Monte Carlo simulations setseed 101 for reproducibility shap -explain fit X subset.
Thus both follow the same steps to overcome the curse of dimensionality. After projection the size of the distorted function domain could be exponentially increased which causes di culty for optimizing the acquisition functions. The projection pursuit paradigm has been applied to density estimation 6 8 12 13 regression 7 and classification 5. Projection pursuit and structure removal was devel-oped into a general projection pursuit paradigm for multivariate analysis by Friedman and Stuetzle 9.
