Set parameters for lazy learning

Description

Set control parameters for a lazy learning object.

Usage

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lazy.control(conIdPar=NULL, linIdPar=1, quaIdPar=NULL,
                distance=c("manhattan","euclidean"), metric=NULL,
                   cmbPar=1, lambda=1e+06)

Arguments

conIdPar

Parameter controlling the number of neighbors to be used for identifying and validating constant models. conIdPar can assume different forms:

conIdPar=c(idm0,idM0,valM0):

In this case, idm0:idM0 is the range in which the best number of neighbors is searched when identifying the local polynomial models of degree 0 and where valM0 is the maximum number of neighbors used for their validation. This means that the constant models identified with k neighbors, are validated on the first v neighbors, where v=min(k,valM0). If valM0=0, valM0 is set to idMO: see next case for details.

conIdPar=c(idm0,idM0):

Here idm0 and idM0 have the same role as in previous case, and valM0 is by default set to idM0: each model is validated on all the neighbors used in identification.

conIdPar=p:

Here idmO and idMO are obtained according to the following formulas: idm0=3 and idMX=5*p. Recommended choice: p=1. As far as the quantity valM0 is concerned, it gets the default value as in previous case.

conIdPar=NULL:

No constant model is considered.

linIdPar

Parameter controlling the number of neighbors to be used for identifying and validating linear models. linIdPar can assume different forms:

linIdPar=c(idm1,idM1,valM1):

In this case, idm1:idM1 is the range in which the best number of neighbors is searched when identifying the local polynomial models of degree 1 and where valM1 is the maximum number of neighbors used for their validation. This means that the linear models identified with k neighbors, are validated on the first v neighbors, where v=min(k,valM1). If valM1=0, valM1 is set to idM1: see next case for details.

linIdPar=c(idm1,idM1):

Here idm1 and idM1 have the same role as in previous case, and valM1 is by default set to idM1: each model is validated on all the neighbors used in identification.

linIdPar=p:

Here idmO and idMO are obtained according to the following formulas: idm1=3*noPar and idM1=5*p*noPar, where noPar=nx+1 is the number of parameter of the polynomial model of degree 1, and nx is the dimensionality of the input space. Recommended choice: p=1. As far as the quantity valM1 is concerned, it gets the default value as in previous case.

linIdPar=NULL:

No linear model is considered.

quaIdPar

Parameter controlling the number of neighbors to be used for identifying and validating quadratic models. quaIdPar can assume different forms:

quaIdPar=c(idm2,idM2,valM2):

In this case, idm2:idM2 is the range in which the best number of neighbors is searched when identifying the local polynomial models of degree 2 and where valM2 is the maximum number of neighbors used for their validation. This means that the quadratic models identified with k neighbors, are validated on the first v neighbors, where v=min(k,valM2). If valM2=0, valM2 is set to idM2: see next case for details.

quaIdPar=c(idm2,idM2):

Here idm2 and idM2 have the same role as in previous case, and valM2 is by default set to idM2: each model is validated on all the neighbors used in identification.

quaIdPar=p:

Here idmO and idMO are obtained according to the following formulas: idm2=3*noPar and idM2=5*p*noPar, where in this case the number of parameters is noPar=(nx+1)*(nx+2)/2, and nx is the dimensionality of the input space. Recommended choice: p=1. As far as the quantity valM2 is concerned, it gets the default value as in previous case.

quaIdPar=NULL:

No quadratic model is considered.

distance

The distance metric: can be manhattan or euclidean.

metric

Vector of n elements. Weights used to evaluate the distance between query point and neighbors.

cmbPar

Parameter controlling the local combination of models. cmbPar can assume different forms:

cmbPar=c(cmb0,cmb1,cmb2):

In this case, cmbX is the number of polynomial models of degree X that will be included in the local combination. Each local model will be therfore a combination of the best cmb0 models of degree 0, the best cmb1 models of degree 1, and the best cmb2 models of degree 2 identified as specified by idPar.

cmbPar=cmb:

Here cmb is the number of models that will be combined, disregarding any constraint on the degree of the models that will be considered. Each local model will be therfore a combination of the best cmb models, identified as specified by id_par.

lambda

Initialization of the diagonal elements of the local variance/covariance matrix for Ridge Regression.

Value

The output of lazy.control is a list containing the following components: conIdPar, linIdPar, quaIdPar, distance, metric, cmbPar, lambda.

Author(s)

Mauro Birattari and Gianluca Bontempi

See Also

lazy, predict.lazy

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