Change hyper-parameters

The purpose of this vignette is to show how the hyper-parameters of models are handled by SDeMo.

using SpeciesDistributionToolkit
using CairoMakie

We will work on the demo data, using Logistic regression.

X, y = SDeMo.__demodata()

([9.200000000000001 8.1 15.5 6.6000000000000005 7.2 9.8 17.0 6.7 12.9 8.6 17.0 16.6 16.8 13.9 12.600000000000001 12.8 8.8 7.800000000000001 15.9 9.1 15.4 10.100000000000001 7.9 16.900000000000002 16.7 9.0 8.4 16.8 16.6 14.200000000000001 11.3 15.4 16.900000000000002 12.8 9.200000000000001 6.0 13.200000000000001 11.4 15.700000000000001 6.9 15.100000000000001 10.600000000000001 15.5 13.0 16.400000000000002 17.1 15.0 10.8 9.1 13.200000000000001 6.1000000000000005 7.4 14.5 16.8 16.7 8.1 8.5 17.0 15.9 15.9 16.900000000000002 17.1 15.8 7.300000000000001 8.700000000000001 14.4 14.0 11.600000000000001 16.900000000000002 16.3 16.0 12.8 13.8 16.0 12.0 8.8 17.0 12.5 16.900000000000002 16.6 16.1 10.200000000000001 9.9 5.9 7.1000000000000005 12.5 16.900000000000002 8.5 16.2 10.700000000000001 13.100000000000001 16.8 7.800000000000001 8.6 9.600000000000001 11.200000000000001 16.5 17.0 14.8 16.400000000000002 13.5 13.3 15.3 16.6 16.8 11.9 9.700000000000001 8.200000000000001 13.5 17.1 14.200000000000001 5.1000000000000005 10.4 9.600000000000001 17.1 11.200000000000001 16.8 9.1 8.9 14.5 11.600000000000001 17.3 6.800000000000001 16.5 10.3 9.1 14.0 16.7 6.300000000000001 17.0 17.0 14.8 13.9 7.300000000000001 9.600000000000001 11.3 15.8 11.100000000000001 16.900000000000002 16.6 17.1 12.700000000000001 11.5 16.7 8.700000000000001 6.6000000000000005 8.9 9.700000000000001 16.3 10.0 9.3 11.0 10.5 14.9 10.5 7.6000000000000005 12.5 13.8 9.200000000000001 15.3 13.100000000000001 9.3 8.3 14.100000000000001 7.6000000000000005 15.200000000000001 10.3 16.8 9.0 9.5 10.100000000000001 16.3 13.4 17.1 10.0 16.2 16.2 8.3 14.700000000000001 16.2 6.9 10.700000000000001 17.0 17.0 14.4 16.3 15.8 16.400000000000002 17.0 15.0 10.700000000000001 10.600000000000001 8.8 16.5 9.200000000000001 16.3 12.700000000000001 13.4 15.700000000000001 12.600000000000001 10.600000000000001 16.900000000000002 16.2 6.6000000000000005 7.9 14.200000000000001 16.400000000000002 16.8 16.900000000000002 16.8 13.9 17.2 5.800000000000001 16.8 14.9 16.0 8.6 13.4 17.1 10.200000000000001 17.1 10.600000000000001 14.4 14.600000000000001 10.200000000000001 16.5 16.900000000000002 9.600000000000001 8.0 16.400000000000002 14.9 6.6000000000000005 10.0 15.8 10.700000000000001 8.200000000000001 11.4 9.4 16.0 10.5 9.5 11.700000000000001 10.5 15.8 8.3 15.8 11.5 12.4 13.4 15.600000000000001 13.0 10.700000000000001 11.700000000000001 12.5 16.7 12.9 10.0 16.900000000000002 14.5 16.8 16.7 8.9 16.400000000000002 16.900000000000002 15.0 15.5 7.1000000000000005 17.0 10.5 15.8 14.3 16.7 12.100000000000001 16.2 14.9 8.4 13.200000000000001 15.3 17.0 12.700000000000001 17.0 6.1000000000000005 14.200000000000001 9.8 16.8 15.200000000000001 11.0 13.9 17.2 10.600000000000001 9.9 7.9 16.5 15.100000000000001 16.0 17.0 15.8 10.4 13.600000000000001 14.200000000000001 9.200000000000001 16.7 15.100000000000001 16.400000000000002 9.8 16.900000000000002 10.0 13.0 12.5 16.5 16.400000000000002 16.900000000000002 10.9 16.8 15.9 14.5 16.900000000000002 16.1 9.3 16.8 12.0 16.1 12.100000000000001 17.1 10.100000000000001 10.600000000000001 10.100000000000001 16.5 7.4 7.6000000000000005 11.3 17.0 16.1 8.8 8.9 7.0 8.5 7.6000000000000005 10.5 14.0 11.100000000000001 5.9 16.900000000000002 16.7 16.6 17.0 13.9 12.600000000000001 16.7 6.800000000000001 10.0 16.2 16.0 10.600000000000001 16.6 14.4 7.7 7.6000000000000005 15.600000000000001 13.5 7.0 9.700000000000001 9.1 10.4 16.7 8.9 3.4000000000000004 16.5 16.6 16.1 10.3 16.6 17.0 14.700000000000001 16.8 7.800000000000001 9.8 7.9 15.8 16.2 10.3 17.1 9.1 10.700000000000001 17.0 9.4 16.6 14.700000000000001 16.900000000000002 16.8 16.900000000000002 16.2 17.1 17.0 5.2 17.0 8.9 14.600000000000001 10.700000000000001 11.0 6.9 16.7 16.7 9.4 11.600000000000001 15.5 13.600000000000001 15.4 14.8 8.3 11.5 15.3 16.6 17.1 8.700000000000001 16.400000000000002 16.5 16.7 13.5 17.0 16.1 17.0 15.9 14.600000000000001 13.5 10.5 15.9 11.9 16.1 14.5 13.8 9.200000000000001 8.9 14.5 17.0 14.100000000000001 13.4 10.8 16.2 16.5 16.900000000000002 11.3 15.200000000000001 8.5 6.6000000000000005 1.0 16.900000000000002 9.0 14.5 9.1 13.8 7.9 8.200000000000001 16.8 13.3 16.400000000000002 8.5 9.200000000000001 17.0 11.600000000000001 11.200000000000001 15.100000000000001 15.9 16.400000000000002 9.200000000000001 15.4 10.600000000000001 13.200000000000001 9.600000000000001 12.9 12.100000000000001 6.2 12.0 15.3 7.1000000000000005 11.0 13.600000000000001 16.3 9.700000000000001 12.5 14.4 7.0 12.3 16.900000000000002 10.700000000000001 16.400000000000002 12.3 17.1 17.0 8.8 11.5 15.3 16.6 13.0 9.3 9.3 14.5 9.0 11.700000000000001 17.0 12.8 14.4 8.8 15.700000000000001 14.0 16.400000000000002 14.9 8.0 7.9 16.5 9.3 16.400000000000002 8.9 9.4 16.0 16.8 16.900000000000002 6.9 11.600000000000001 12.8 12.9 16.8 8.200000000000001 8.9 9.600000000000001 12.9 16.2 14.200000000000001 15.0 16.7 10.700000000000001 8.200000000000001 7.4 8.9 8.700000000000001 11.100000000000001 6.9 14.4 17.0 17.0 15.8 14.0 14.0 10.5 16.5 8.1 9.5 7.2 13.700000000000001 10.700000000000001 13.8 14.0 14.9 10.8 9.0 14.9 8.0 14.5 16.3 6.1000000000000005 16.8 17.0 13.100000000000001 10.200000000000001 8.4 8.9 9.200000000000001 13.5 14.4 13.4 11.200000000000001 9.3 7.800000000000001 16.8 7.5 13.3 16.2 16.5 17.0 16.3 15.700000000000001 16.1 7.6000000000000005 10.0 11.100000000000001 14.200000000000001 8.5 16.5 7.5 12.0 7.800000000000001 14.0 9.4 10.4 16.400000000000002 16.3 10.5 16.5 15.5 16.900000000000002 15.200000000000001 14.8 8.3 15.5 7.6000000000000005 14.100000000000001 9.9 11.0 10.8 12.0 8.4 8.700000000000001 15.3 16.5 9.3 12.200000000000001 11.9 17.0 10.700000000000001 14.100000000000001 10.5 13.200000000000001 6.6000000000000005 10.8 8.6 9.0 9.5 16.5 9.3 11.4 10.0 6.7 10.4 8.8 9.3 16.400000000000002 3.7 16.0 9.600000000000001 15.4 16.900000000000002 11.9 10.700000000000001 7.7 8.5 16.8 13.8 9.8 11.4 15.600000000000001 7.1000000000000005 11.4 17.1 15.8 10.700000000000001 14.700000000000001 6.300000000000001 14.5 14.100000000000001 8.4 15.4 6.9 16.0 6.1000000000000005 10.100000000000001 16.900000000000002 15.8 13.600000000000001 10.3 8.200000000000001 12.700000000000001 12.700000000000001 14.5 16.6 17.1 14.4 9.4 14.9 15.5 11.200000000000001 16.1 8.6 15.700000000000001 9.3 9.8 4.1000000000000005 9.600000000000001 8.3 16.400000000000002 15.8 15.8 13.100000000000001 14.3 7.2 16.900000000000002 9.3 16.8 16.7 17.0 14.4 16.7 16.0 12.0 12.0 15.9 13.700000000000001 7.800000000000001 16.7 7.5 14.600000000000001 10.5 9.1 16.7 15.700000000000001 17.0 12.8 8.700000000000001 8.5 16.5 15.8 10.8 14.4 13.600000000000001 8.8 14.600000000000001 14.3 16.1 16.2 10.100000000000001 9.3 16.900000000000002 17.1 8.200000000000001 13.4 14.9 17.0 17.2 16.0 13.3 16.6 15.3 16.6 15.9 17.0 16.1 15.4 15.4 11.600000000000001 15.0 12.9 9.700000000000001 12.600000000000001 7.7 9.9 10.600000000000001 11.5 7.6000000000000005 15.4 9.4 10.600000000000001 14.9 6.2 10.5 15.3 14.100000000000001 14.5 8.4 16.5 17.0 12.8 16.3 11.200000000000001 10.600000000000001 12.5 8.4 9.4 10.100000000000001 16.2 15.9 10.0 16.900000000000002 11.5 15.9 12.8 16.3 14.8 7.5 8.1 15.0 4.2 11.9 12.100000000000001 14.9 13.8 10.200000000000001 9.9 7.300000000000001 14.700000000000001 16.7 15.0 13.4 13.4 9.3 16.3 9.5 3.3000000000000003 6.9 14.5 13.5 16.7 11.600000000000001 7.300000000000001 10.100000000000001 17.2 10.0 8.5 12.5 10.600000000000001 10.9 7.800000000000001 11.200000000000001 9.0 13.4 7.0 6.300000000000001 10.4 16.8 15.700000000000001 14.3 8.200000000000001 8.0 9.5 16.8 15.100000000000001 14.100000000000001 16.3 10.8 16.7 10.0 13.8 8.4 8.1 17.2 11.700000000000001 16.6 8.5 10.600000000000001 15.200000000000001 13.9 16.900000000000002 17.0 8.8 15.200000000000001 8.6 14.9 9.3 16.3 8.200000000000001 9.1 16.900000000000002 14.600000000000001 16.400000000000002 10.4 10.200000000000001 16.1 6.2 11.200000000000001 13.3 15.200000000000001 12.5 9.600000000000001 9.4 8.4 15.100000000000001 9.4 8.700000000000001 11.700000000000001 8.1 16.5 13.200000000000001 9.4 16.900000000000002 12.700000000000001 17.1 13.9 16.8 11.4 13.600000000000001 12.4 13.5 17.1 11.100000000000001 14.9 16.400000000000002 17.1 8.3 10.100000000000001 16.6 13.0 8.200000000000001 16.900000000000002 15.700000000000001 10.3 8.1 14.8 9.600000000000001 6.7 6.4 8.9 5.7 9.4 15.600000000000001 14.5 15.600000000000001 13.0 16.8 17.0 15.4 6.9 10.100000000000001 9.600000000000001 15.9 9.4 6.7 7.9 12.700000000000001 7.0 7.9 9.3 14.100000000000001 16.900000000000002 11.100000000000001 17.2 12.8 7.6000000000000005 15.700000000000001 16.900000000000002 16.0 12.3 15.700000000000001 15.3 7.6000000000000005 16.7 10.700000000000001 12.8 8.8 9.3 12.100000000000001 14.200000000000001 8.4 16.900000000000002 8.700000000000001 11.200000000000001 9.8 8.0 8.700000000000001 11.100000000000001 10.9 14.600000000000001 8.5 9.3 14.3 13.200000000000001 8.4 10.0 16.7 16.8 12.700000000000001 14.9 16.5 5.5 16.900000000000002 16.900000000000002 8.4 11.4 16.900000000000002 15.600000000000001 16.3 13.700000000000001 9.200000000000001 16.0 13.9 16.0 15.8 8.1 12.600000000000001 16.900000000000002 9.200000000000001 16.900000000000002 16.8 4.0 11.600000000000001 5.5 16.0 16.7 8.5 13.5 10.5 11.3 14.3 5.9 15.200000000000001 9.0 16.400000000000002 9.700000000000001 8.0 16.900000000000002 12.200000000000001 10.100000000000001 16.6 10.8 7.800000000000001 10.4 14.9 12.5 14.8 16.400000000000002 8.4 15.4 3.5 9.0 13.4 17.0 9.600000000000001 16.0 16.8 12.4 7.1000000000000005 9.200000000000001 11.3 15.4 9.0 8.5 16.900000000000002 14.4 9.9 16.900000000000002 17.1 15.0 11.100000000000001 15.100000000000001 15.0 16.6 15.0 11.700000000000001 13.8 16.900000000000002 9.5 15.5 16.5 16.1 10.9 9.1 12.9 13.4 17.2 16.7 13.600000000000001 16.8 12.4 9.200000000000001 13.5 6.7 17.2 11.3 16.900000000000002 12.100000000000001 7.800000000000001 7.0 7.6000000000000005 16.900000000000002 8.9 11.100000000000001 8.8 10.0 15.4 11.0 9.0 12.100000000000001 10.3 8.6 16.7 8.4 7.300000000000001 16.1 12.0 10.0 12.600000000000001 16.900000000000002 13.200000000000001 16.8 13.8 16.7 16.900000000000002 7.6000000000000005 17.0 8.200000000000001 12.3 17.1 15.4 14.600000000000001 15.100000000000001 11.100000000000001 12.0 16.8 1.8 8.1 6.800000000000001 8.3 17.0 15.3 13.5 12.3 12.200000000000001 9.700000000000001 10.4 11.700000000000001 16.6 9.0 16.6 16.400000000000002 17.2 9.4 10.700000000000001 9.1 16.900000000000002 9.3 8.700000000000001 16.7 8.8 15.0 14.8 17.0 10.200000000000001 8.200000000000001 13.9 11.8 15.700000000000001 8.6 16.5 13.700000000000001 12.700000000000001 6.6000000000000005 14.700000000000001 8.6 13.5 11.100000000000001 16.6 16.900000000000002 13.5 15.9 7.800000000000001 15.600000000000001 10.4 13.8 16.8 5.0 16.5 15.100000000000001 15.3 15.9 8.4 15.200000000000001 14.9 15.600000000000001 12.600000000000001 10.3 7.6000000000000005 15.8 16.8 16.2 14.700000000000001 11.100000000000001 7.300000000000001 13.100000000000001 13.8 13.700000000000001 12.600000000000001 17.1 15.9 14.5 11.600000000000001 10.700000000000001 17.1 16.3 15.200000000000001 16.400000000000002 14.4 10.0 12.200000000000001 15.200000000000001 14.700000000000001 9.0 12.600000000000001 13.8 15.200000000000001 7.7 9.4 9.600000000000001 16.900000000000002 11.200000000000001 11.9 16.6 8.1 9.8 9.700000000000001 8.700000000000001 7.1000000000000005 13.700000000000001 16.7 8.200000000000001 17.1 14.3 8.8 16.2 15.9 10.100000000000001 14.8 16.400000000000002 10.9 8.700000000000001 9.9 14.3 16.8 9.9 17.1 16.900000000000002 13.4 14.100000000000001 17.0 9.3 16.0 15.600000000000001 17.0 15.4 14.5 15.8 14.100000000000001 6.800000000000001 15.600000000000001 9.0 14.3 9.3 15.700000000000001 12.5 12.100000000000001 12.600000000000001 15.200000000000001 7.2 7.1000000000000005 11.8 16.8 10.100000000000001 15.700000000000001 5.800000000000001 16.3 17.2 11.4 6.300000000000001 7.5 15.0 15.9 12.9 10.9 7.300000000000001 15.600000000000001 15.3 7.6000000000000005 8.4 15.200000000000001 10.9 15.600000000000001 5.300000000000001 11.9 16.3 16.900000000000002 13.3 16.8 11.600000000000001 9.8 16.8 16.8 7.9 11.8 11.9 13.5 16.8 9.9 16.5 15.9 17.0 9.700000000000001 15.600000000000001 11.0 16.0 10.100000000000001 11.5 17.2 17.1 9.5 11.0 10.3 7.4 15.0 14.4 13.3 14.600000000000001 9.600000000000001 11.9 13.8 16.3 15.600000000000001 7.1000000000000005 6.1000000000000005 15.200000000000001 7.300000000000001 9.600000000000001 12.600000000000001 8.3 9.3 16.2 17.2 14.9 16.7 12.100000000000001 14.200000000000001 16.5 14.100000000000001 11.200000000000001 13.100000000000001 4.7 9.4 17.0 10.9 11.0 5.800000000000001 10.700000000000001 17.0 16.900000000000002 7.800000000000001 16.900000000000002 16.2 16.5 16.8 13.200000000000001 17.1 11.0 11.200000000000001 9.200000000000001 16.0 17.0 13.9 11.8 16.3 12.100000000000001 16.400000000000002 15.3 10.9 8.5 13.9 9.0 14.700000000000001 5.300000000000001 12.200000000000001 16.7 7.4 16.2 14.4 4.1000000000000005 9.3 8.700000000000001 15.200000000000001 8.8 7.6000000000000005 15.700000000000001 9.4 16.7 7.6000000000000005 16.8 16.5 16.400000000000002 17.0 9.200000000000001 16.5 7.4 15.4 9.600000000000001 16.900000000000002 16.900000000000002 6.2 17.1 17.0 16.7 9.4 16.900000000000002 9.600000000000001 14.4 16.8 13.600000000000001 9.3 11.9 16.1 13.200000000000001 16.8 16.7 16.2 14.600000000000001 5.1000000000000005 10.100000000000001 9.1 11.700000000000001 12.600000000000001 16.3 15.5 9.4 8.4 10.100000000000001 11.0 13.9 9.600000000000001 12.700000000000001 12.0 10.100000000000001 13.600000000000001 14.3 8.8 6.9 11.700000000000001 16.400000000000002 10.700000000000001 8.8 8.1 11.9 9.0 7.9 14.700000000000001 16.900000000000002 16.0 10.700000000000001 11.5 16.6 13.9 15.600000000000001 12.9 16.400000000000002 16.7 14.8 15.200000000000001 9.0 16.900000000000002 5.9 16.7 15.3 16.400000000000002 13.5 17.0 8.6 6.300000000000001 7.2 15.0 10.4 12.600000000000001 17.0 14.0 9.200000000000001 15.3 16.8 10.700000000000001 16.6 15.100000000000001 17.1 13.9 9.0 7.2 8.1 16.1 10.9 15.9 14.100000000000001 8.0 11.3 7.5 16.6 13.9; 4.2 4.1000000000000005 3.5 3.8000000000000003 4.3 3.4000000000000004 3.4000000000000004 3.9000000000000004 2.8000000000000003 3.9000000000000004 3.7 3.4000000000000004 3.7 4.1000000000000005 4.2 4.3 4.1000000000000005 4.1000000000000005 3.7 2.7 3.4000000000000004 4.2 4.2 2.9000000000000004 3.3000000000000003 4.0 4.0 4.0 4.1000000000000005 3.6 4.0 4.0 3.1 3.5 4.1000000000000005 3.8000000000000003 3.6 3.3000000000000003 3.9000000000000004 3.7 4.1000000000000005 4.2 2.6 2.9000000000000004 4.2 3.8000000000000003 3.9000000000000004 4.0 3.3000000000000003 4.2 4.3 3.6 3.9000000000000004 3.7 2.5 3.9000000000000004 3.9000000000000004 3.6 3.9000000000000004 3.3000000000000003 3.0 4.0 2.7 4.2 3.5 2.6 3.9000000000000004 3.9000000000000004 3.4000000000000004 3.2 3.3000000000000003 3.9000000000000004 2.6 3.8000000000000003 4.0 4.3 3.4000000000000004 4.1000000000000005 3.0 3.7 2.7 3.4000000000000004 4.2 4.0 3.5 4.1000000000000005 3.2 3.5 4.1000000000000005 3.6 4.2 3.1 3.9000000000000004 3.7 4.3 4.2 3.2 3.7 3.9000000000000004 2.7 3.6 3.5 3.9000000000000004 4.2 2.5 4.2 4.2 4.1000000000000005 4.0 3.8000000000000003 3.7 3.7 4.0 3.8000000000000003 3.8000000000000003 3.4000000000000004 2.5 4.0 4.2 4.1000000000000005 3.8000000000000003 3.6 3.9000000000000004 3.8000000000000003 3.8000000000000003 4.3 3.6 3.0 4.2 3.7 3.7 4.4 4.2 3.5 4.3 3.6 3.4000000000000004 4.2 3.7 2.6 3.9000000000000004 3.3000000000000003 3.5 3.5 4.3 4.1000000000000005 4.2 4.0 3.9000000000000004 4.0 4.4 4.0 4.3 3.9000000000000004 4.3 4.0 3.2 3.9000000000000004 3.8000000000000003 4.2 4.2 4.0 3.9000000000000004 3.9000000000000004 4.0 4.0 3.7 3.0 3.9000000000000004 3.8000000000000003 4.1000000000000005 3.2 4.0 4.1000000000000005 4.3 3.1 2.7 4.0 3.4000000000000004 3.9000000000000004 4.0 3.9000000000000004 3.9000000000000004 3.5 4.2 3.2 2.5 4.1000000000000005 3.8000000000000003 4.3 4.1000000000000005 3.7 4.0 3.2 4.1000000000000005 3.2 3.8000000000000003 4.2 3.3000000000000003 4.2 4.3 3.7 2.6 4.1000000000000005 4.0 3.5 3.9000000000000004 3.1 3.1 3.3000000000000003 3.9000000000000004 3.6 3.9000000000000004 3.1 3.4000000000000004 2.6 4.2 2.7 3.7 4.2 3.8000000000000003 4.3 3.3000000000000003 4.2 2.8000000000000003 4.0 3.4000000000000004 4.3 3.7 3.3000000000000003 3.9000000000000004 4.2 4.0 3.8000000000000003 4.1000000000000005 4.2 4.1000000000000005 4.0 3.3000000000000003 2.6 4.3 4.1000000000000005 3.6 3.9000000000000004 4.2 2.9000000000000004 2.7 3.9000000000000004 4.4 3.2 3.3000000000000003 4.0 3.2 4.4 2.5 3.9000000000000004 3.8000000000000003 3.1 3.0 3.3000000000000003 3.6 4.1000000000000005 3.9000000000000004 3.4000000000000004 4.1000000000000005 3.9000000000000004 4.0 3.8000000000000003 4.3 2.7 3.7 2.5 4.0 3.1 4.3 3.7 3.6 3.2 3.7 3.5 3.5 3.8000000000000003 3.2 4.2 4.1000000000000005 3.8000000000000003 4.1000000000000005 4.1000000000000005 3.7 4.3 3.7 4.3 3.6 3.3000000000000003 3.7 3.7 2.7 4.3 4.2 4.0 3.6 4.1000000000000005 2.5 4.1000000000000005 4.3 4.2 3.4000000000000004 4.3 4.2 3.8000000000000003 4.1000000000000005 3.8000000000000003 3.4000000000000004 3.1 4.0 3.9000000000000004 3.0 3.0 4.0 4.1000000000000005 4.0 4.2 4.3 3.7 2.7 4.1000000000000005 3.4000000000000004 4.2 4.1000000000000005 4.2 3.9000000000000004 3.4000000000000004 3.1 3.9000000000000004 4.2 4.2 4.3 4.0 4.2 4.2 4.3 3.9000000000000004 3.5 3.9000000000000004 3.7 3.7 4.3 2.6 2.5 3.8000000000000003 4.0 2.6 3.8000000000000003 4.0 3.5 2.6 4.2 4.0 3.3000000000000003 3.7 4.2 3.7 4.1000000000000005 4.1000000000000005 4.1000000000000005 4.2 4.2 3.8000000000000003 3.4000000000000004 3.9000000000000004 4.2 3.9000000000000004 3.7 4.2 3.9000000000000004 3.9000000000000004 3.7 3.5 2.9000000000000004 2.9000000000000004 3.3000000000000003 3.7 4.0 4.3 3.8000000000000003 4.2 4.1000000000000005 4.3 3.3000000000000003 3.2 3.9000000000000004 2.6 4.1000000000000005 3.9000000000000004 4.1000000000000005 3.7 4.3 3.7 4.3 3.7 3.9000000000000004 2.5 3.9000000000000004 4.0 4.3 4.2 3.8000000000000003 3.2 4.1000000000000005 3.9000000000000004 4.1000000000000005 4.0 3.8000000000000003 3.7 3.5 3.1 4.1000000000000005 4.1000000000000005 3.6 4.0 2.8000000000000003 3.8000000000000003 3.9000000000000004 3.9000000000000004 3.0 4.4 4.1000000000000005 3.5 3.9000000000000004 4.2 4.2 4.2 4.3 3.2 3.7 3.4000000000000004 3.4000000000000004 4.1000000000000005 3.8000000000000003 3.7 3.3000000000000003 3.3000000000000003 3.9000000000000004 4.4 4.3 3.8000000000000003 3.1 3.9000000000000004 3.9000000000000004 3.7 4.1000000000000005 4.4 3.9000000000000004 4.1000000000000005 4.4 3.1 4.0 3.5 3.9000000000000004 4.4 4.1000000000000005 4.1000000000000005 3.5 3.0 4.1000000000000005 3.4000000000000004 4.1000000000000005 3.9000000000000004 4.2 4.0 3.4000000000000004 4.0 3.3000000000000003 3.4000000000000004 4.2 4.0 2.9000000000000004 4.0 4.2 4.3 2.5 3.9000000000000004 4.3 4.2 3.5 3.9000000000000004 4.3 3.8000000000000003 3.7 4.1000000000000005 4.4 3.2 2.6 3.2 4.4 4.0 2.7 4.1000000000000005 3.5 3.8000000000000003 3.2 4.2 3.6 3.9000000000000004 4.0 3.5 4.1000000000000005 4.0 4.3 2.7 4.3 3.3000000000000003 4.3 3.9000000000000004 4.1000000000000005 3.9000000000000004 3.0 3.7 4.1000000000000005 4.1000000000000005 4.3 3.7 4.4 4.2 3.5 4.2 4.2 4.0 4.4 3.7 3.9000000000000004 3.2 3.9000000000000004 4.0 4.0 4.0 4.3 3.9000000000000004 3.4000000000000004 3.8000000000000003 3.9000000000000004 3.7 3.1 3.7 4.3 3.5 4.2 4.0 4.3 2.7 3.4000000000000004 3.1 3.4000000000000004 4.0 3.9000000000000004 3.6 4.1000000000000005 3.0 3.4000000000000004 4.0 3.3000000000000003 3.7 4.0 3.6 4.3 4.1000000000000005 4.2 3.3000000000000003 4.2 2.9000000000000004 4.4 4.1000000000000005 3.9000000000000004 2.7 4.2 4.2 3.9000000000000004 3.8000000000000003 3.8000000000000003 3.7 3.9000000000000004 3.4000000000000004 3.5 4.0 3.8000000000000003 2.7 4.2 4.1000000000000005 3.9000000000000004 2.7 3.9000000000000004 3.9000000000000004 4.1000000000000005 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1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0])

We will limit the model to two variables for the sake of illustration.

sdm = SDM(ZScore, Logistic, X, y)
variables!(sdm, [1, 12])

2-element Vector{Int64}:
  1
 12

We can inspect the hyper-parameters for the transformer:

hyperparameters(transformer(sdm))

and for the classifier:

hyperparameters(classifier(sdm))

Dict{Symbol, Any} with 6 entries:
  :verbose => false
  :interactions => :all
  :λ => 0.1
  :verbosity => 100
  :epochs => 2000
  :η => 0.01

If we want to turn off the interactions between variables while keeping the squared terms, we can use:

hyperparameters!(classifier(sdm), :interactions, :self)
train!(sdm)

"2.67 - 1.82 × 𝐱₁ - 0.56 × 𝐱₂ - 0.16 × (𝐱₁)² - 0.01 × (𝐱₂)²"

We can similarly tweak the learning rate η (lower is slower but more stable), the regularization parameter λ, and the number of epochs used for training. For example, if we want slower learning with more penalty for large parameters, we can write

hyperparameters!(classifier(sdm), :interactions, :all)
hyperparameters!(classifier(sdm), :η, 1e-4)
hyperparameters!(classifier(sdm), :λ, 0.2)
hyperparameters!(classifier(sdm), :epochs, 10_000)
train!(sdm)

"1.97 - 1.58 × 𝐱₁ - 0.42 × 𝐱₂ - 0.27 × (𝐱₁)² - 0.06 × (𝐱₂)² - 0.06 × (𝐱₁⋅𝐱₂)"

Note that logistic regression has a :verbose hyper-parameter to turn output on training epochs on and off.

Related documentation

SDeMo.hyperparameters Function

hyperparameters(::Type{<:HasHyperParams}) = nothing

Returns the hyper-parameters for a type of classifier or transformer

source

hyperparameters(::HasHyperParams, ::Symbol)

Returns the value for an hyper-parameter

source

hyperparameters(::HasHyperParams)

Returns the hyper-parameters for a classifier or a transformer

source

SDeMo.hyperparameters! Function

hyperparameters!(tr::HasHyperParams, hp::Symbol, val)

Sets the hyper-parameters for a transformer or a classifier

source