Description Usage Arguments Details Examples

Can be used to check model convergence.

1 | ```
convergence_plot(model, Xtest, ytest, burnin = 0)
``` |

`model` |
list containing a model of class "hs_rulefit". |

`Xtest` |
Out of bag sample to check error. |

`ytest` |
response of test data. |

`burnin` |
Number of samples disregarded as burnin. |

Convergence is checked by the convergence of the prediction error on unseen test data, to find a suitable number of iterations, in the spirit of gradient boosting. To check convergence on the Training data just use training X and y instead of Xtest and ytest.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
library(MASS)
data(Boston)
#Split in train and test data
N = nrow(Boston)
train = sample(1:N, 400)
Xtrain = Boston[train,-14]
ytrain = Boston[train, 14]
Xtest = Boston[-train, -14]
ytest = Boston[-train, 14]
hrres = HorseRuleFit(X = Xtrain, y=ytrain,
thin=1, niter=100, burnin=10,
L=5, S=6, ensemble = "both", mix=0.3, ntree=100,
intercept=FALSE, linterms=1:13, ytransform = "log",
alpha=1, beta=2, linp = 1, restricted = 0)
#Check the model convergence out of sample
convergence_plot(hrres, Xtest, ytest, burnin = 10)
``` |

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