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Data-driven cost range for LS-SVR psvr models

cost_psvr_ls_data.Rd

Returns a quant_param whose search range scales with var(y) * N, the standard heuristic for the LS-SVR regularisation parameter \(\Gamma\) (Suykens et al. 2002, Least Squares Support Vector Machines, §3.1.3). On the log2 scale, the lower bound is -2 (i.e. \(\Gamma \ge 0.25\)) and the upper bound is log2(var(y) * N) + width_log2.

Usage

cost_psvr_ls_data(y, n = length(y), width_log2 = 4)

Arguments

y

Numeric vector of strictly positive training targets.

n

Sample size. Default length(y).

width_log2

Scalar giving the half-width (in log2 units) added above log2(var(y) * n) to set the upper bound. Default 4 (about 16× headroom). Negative values are accepted with a warning, since the resulting upper bound falls below the var(y) * n heuristic and is unlikely to be useful.

Value

A quant_param dials object.

Details

The default width_log2 = 4 places the upper bound about 16 times above var(y) * n, which is headroom for a Bayesian or grid search to work in without pinning against the boundary. Because the bound tracks var(y) * n, it moves with the outcome: the static cost_psvr() range [-2, 10] (i.e. \(\Gamma \le 1024\)) is fixed, so it falls short whenever var(y) * n exceeds a few hundred — which is the usual case, not the exception.

Use this function for m3 (LS-SVR) and m4 (symmetric LS-SVR) workflows. Stick to cost_psvr() for m1/m2 (\(\epsilon\)-SVR), where cost maps to C and typical optima lie in [10, 100].

See also

cost_psvr(), psvr_option_add_cost_ls()

Examples

cost_psvr_ls_data(c(10, 20, 30, 40, 50))
#> Cost (quantitative)
#> Transformer: log-2 [1e-100, Inf]
#> Range (transformed scale): [-2, 14.3]