This function visualizes the risk during training. If validation data are given, then the train risk is plotted against the validation risk.
Examples
cboost_no_valdat = boostSplines(data = iris, target = "Sepal.Length",
loss = LossQuadratic$new())
#> 1/100 risk = 0.31 time = 0
#> 2/100 risk = 0.29 time = 14965
#> 4/100 risk = 0.25 time = 44911
#> 6/100 risk = 0.21 time = 72098
#> 8/100 risk = 0.18 time = 100091
#> 10/100 risk = 0.16 time = 111518
#> 12/100 risk = 0.14 time = 111701
#> 14/100 risk = 0.13 time = 111860
#> 16/100 risk = 0.11 time = 112019
#> 18/100 risk = 0.1 time = 112192
#> 20/100 risk = 0.095 time = 112350
#> 22/100 risk = 0.088 time = 112495
#> 24/100 risk = 0.083 time = 112681
#> 26/100 risk = 0.078 time = 112837
#> 28/100 risk = 0.074 time = 112993
#> 30/100 risk = 0.071 time = 113150
#> 32/100 risk = 0.069 time = 113308
#> 34/100 risk = 0.067 time = 113476
#> 36/100 risk = 0.065 time = 113650
#> 38/100 risk = 0.063 time = 113802
#> 40/100 risk = 0.062 time = 113942
#> 42/100 risk = 0.061 time = 114081
#> 44/100 risk = 0.06 time = 114232
#> 46/100 risk = 0.059 time = 114370
#> 48/100 risk = 0.058 time = 114508
#> 50/100 risk = 0.057 time = 114647
#> 52/100 risk = 0.056 time = 114789
#> 54/100 risk = 0.056 time = 114930
#> 56/100 risk = 0.055 time = 115077
#> 58/100 risk = 0.054 time = 115207
#> 60/100 risk = 0.054 time = 115336
#> 62/100 risk = 0.053 time = 115467
#> 64/100 risk = 0.052 time = 115599
#> 66/100 risk = 0.052 time = 115761
#> 68/100 risk = 0.051 time = 115901
#> 70/100 risk = 0.051 time = 116052
#> 72/100 risk = 0.051 time = 116213
#> 74/100 risk = 0.05 time = 116355
#> 76/100 risk = 0.05 time = 116498
#> 78/100 risk = 0.049 time = 116629
#> 80/100 risk = 0.049 time = 116760
#> 82/100 risk = 0.049 time = 116891
#> 84/100 risk = 0.048 time = 117021
#> 86/100 risk = 0.048 time = 117154
#> 88/100 risk = 0.048 time = 117287
#> 90/100 risk = 0.048 time = 117418
#> 92/100 risk = 0.047 time = 117549
#> 94/100 risk = 0.047 time = 117679
#> 96/100 risk = 0.047 time = 117810
#> 98/100 risk = 0.047 time = 117943
#> 100/100 risk = 0.046 time = 118076
#>
#>
#> Train 100 iterations in 0 Seconds.
#> Final risk based on the train set: 0.046
#>
plotRisk(cboost_no_valdat)
cboost_valdat = boostSplines(data = iris, target = "Sepal.Length",
loss = LossQuadratic$new(), oob_fraction = 0.3)
#> 1/100 risk = 0.32 oob_risk = 0.28 time = 0
#> 2/100 risk = 0.3 oob_risk = 0.26 time = 107
#> 4/100 risk = 0.25 oob_risk = 0.23 time = 242
#> 6/100 risk = 0.21 oob_risk = 0.2 time = 376
#> 8/100 risk = 0.18 oob_risk = 0.18 time = 521
#> 10/100 risk = 0.16 oob_risk = 0.16 time = 634
#> 12/100 risk = 0.14 oob_risk = 0.15 time = 754
#> 14/100 risk = 0.12 oob_risk = 0.13 time = 862
#> 16/100 risk = 0.11 oob_risk = 0.12 time = 970
#> 18/100 risk = 0.1 oob_risk = 0.12 time = 1081
#> 20/100 risk = 0.091 oob_risk = 0.11 time = 1191
#> 22/100 risk = 0.084 oob_risk = 0.11 time = 1301
#> 24/100 risk = 0.078 oob_risk = 0.1 time = 1411
#> 26/100 risk = 0.073 oob_risk = 0.099 time = 1550
#> 28/100 risk = 0.069 oob_risk = 0.096 time = 1678
#> 30/100 risk = 0.066 oob_risk = 0.094 time = 1835
#> 32/100 risk = 0.063 oob_risk = 0.093 time = 1962
#> 34/100 risk = 0.061 oob_risk = 0.091 time = 2093
#> 36/100 risk = 0.059 oob_risk = 0.09 time = 2235
#> 38/100 risk = 0.057 oob_risk = 0.09 time = 2401
#> 40/100 risk = 0.056 oob_risk = 0.089 time = 2528
#> 42/100 risk = 0.055 oob_risk = 0.088 time = 2678
#> 44/100 risk = 0.053 oob_risk = 0.088 time = 2805
#> 46/100 risk = 0.052 oob_risk = 0.087 time = 2914
#> 48/100 risk = 0.051 oob_risk = 0.087 time = 3023
#> 50/100 risk = 0.05 oob_risk = 0.086 time = 3131
#> 52/100 risk = 0.049 oob_risk = 0.086 time = 3253
#> 54/100 risk = 0.048 oob_risk = 0.086 time = 3356
#> 56/100 risk = 0.048 oob_risk = 0.085 time = 3461
#> 58/100 risk = 0.047 oob_risk = 0.085 time = 3563
#> 60/100 risk = 0.046 oob_risk = 0.085 time = 3671
#> 62/100 risk = 0.046 oob_risk = 0.085 time = 3776
#> 64/100 risk = 0.045 oob_risk = 0.084 time = 3880
#> 66/100 risk = 0.045 oob_risk = 0.084 time = 3986
#> 68/100 risk = 0.044 oob_risk = 0.084 time = 4092
#> 70/100 risk = 0.044 oob_risk = 0.084 time = 4198
#> 72/100 risk = 0.043 oob_risk = 0.084 time = 4303
#> 74/100 risk = 0.043 oob_risk = 0.084 time = 4407
#> 76/100 risk = 0.042 oob_risk = 0.083 time = 4510
#> 78/100 risk = 0.042 oob_risk = 0.083 time = 4614
#> 80/100 risk = 0.042 oob_risk = 0.083 time = 4719
#> 82/100 risk = 0.041 oob_risk = 0.083 time = 4825
#> 84/100 risk = 0.041 oob_risk = 0.083 time = 4929
#> 86/100 risk = 0.041 oob_risk = 0.083 time = 5053
#> 88/100 risk = 0.041 oob_risk = 0.083 time = 5173
#> 90/100 risk = 0.04 oob_risk = 0.083 time = 5295
#> 92/100 risk = 0.04 oob_risk = 0.083 time = 5409
#> 94/100 risk = 0.04 oob_risk = 0.083 time = 5523
#> 96/100 risk = 0.04 oob_risk = 0.083 time = 5634
#> 98/100 risk = 0.039 oob_risk = 0.082 time = 5746
#> 100/100 risk = 0.039 oob_risk = 0.082 time = 5860
#>
#>
#> Train 100 iterations in 0 Seconds.
#> Final risk based on the train set: 0.039
#>
plotRisk(cboost_valdat)
