GLM with Elastic Net Regularization Survival Learner
mlr_learners_surv.glmnet.RdGeneralized linear models with elastic net regularization.
Calls glmnet::glmnet() from package glmnet.
Prediction types
This learner returns three prediction types:
lp: a vector containing the linear predictors (relative risk scores), where each score corresponds to a specific test observation. Calculated usingglmnet::predict.coxnet().crank: same aslp.distr: a survival matrix in two dimensions, where observations are represented in rows and time points in columns. Calculated usingglmnet::survfit.coxnet(). Parametersstypeandctyperelate to howlppredictions are transformed into survival predictions and are described insurvival::survfit.coxph(). By default the Breslow estimator is used for computing the baseline hazard.
Caution: This learner is different to learners calling glmnet::cv.glmnet()
in that it does not use the internal optimization of parameter lambda.
Instead, lambda needs to be tuned by the user (e.g., via mlr3tuning).
When lambda is tuned, the glmnet will be trained for each tuning iteration.
While fitting the whole path of lambdas would be more efficient, as is done
by default in glmnet::glmnet(), tuning/selecting the parameter at prediction time
(using parameter s) is currently not supported in mlr3
(at least not in efficient manner).
Tuning the s parameter is, therefore, currently discouraged.
When the data are i.i.d. and efficiency is key, we recommend using the respective
auto-tuning counterpart in mlr_learners_surv.cv_glmnet().
However, in some situations this is not applicable, usually when data are
imbalanced or not i.i.d. (longitudinal, time-series) and tuning requires
custom resampling strategies (blocked design, stratification).
Meta Information
Task type: “surv”
Predict Types: “crank”, “distr”, “lp”
Feature Types: “logical”, “integer”, “numeric”
Required Packages: mlr3, mlr3proba, mlr3extralearners, glmnet
Parameters
| Id | Type | Default | Levels | Range |
| alignment | character | lambda | lambda, fraction | - |
| alpha | numeric | 1 | \([0, 1]\) | |
| big | numeric | 9.9e+35 | \((-\infty, \infty)\) | |
| devmax | numeric | 0.999 | \([0, 1]\) | |
| dfmax | integer | - | \([0, \infty)\) | |
| eps | numeric | 1e-06 | \([0, 1]\) | |
| epsnr | numeric | 1e-08 | \([0, 1]\) | |
| exact | logical | FALSE | TRUE, FALSE | - |
| exclude | untyped | - | - | |
| exmx | numeric | 250 | \((-\infty, \infty)\) | |
| fdev | numeric | 1e-05 | \([0, 1]\) | |
| gamma | untyped | - | - | |
| grouped | logical | TRUE | TRUE, FALSE | - |
| intercept | logical | TRUE | TRUE, FALSE | - |
| keep | logical | FALSE | TRUE, FALSE | - |
| lambda | untyped | - | - | |
| lambda.min.ratio | numeric | - | \([0, 1]\) | |
| lower.limits | untyped | -Inf | - | |
| maxit | integer | 100000 | \([1, \infty)\) | |
| mnlam | integer | 5 | \([1, \infty)\) | |
| mxit | integer | 100 | \([1, \infty)\) | |
| mxitnr | integer | 25 | \([1, \infty)\) | |
| newoffset | untyped | - | - | |
| nlambda | integer | 100 | \([1, \infty)\) | |
| offset | untyped | NULL | - | |
| parallel | logical | FALSE | TRUE, FALSE | - |
| penalty.factor | untyped | - | - | |
| pmax | integer | - | \([0, \infty)\) | |
| pmin | numeric | 1e-09 | \([0, 1]\) | |
| prec | numeric | 1e-10 | \((-\infty, \infty)\) | |
| predict.gamma | numeric | gamma.1se | \((-\infty, \infty)\) | |
| relax | logical | FALSE | TRUE, FALSE | - |
| s | numeric | 0.01 | \([0, \infty)\) | |
| standardize | logical | TRUE | TRUE, FALSE | - |
| thresh | numeric | 1e-07 | \([0, \infty)\) | |
| trace.it | integer | 0 | \([0, 1]\) | |
| type.logistic | character | Newton | Newton, modified.Newton | - |
| type.multinomial | character | ungrouped | ungrouped, grouped | - |
| upper.limits | untyped | Inf | - | |
| stype | integer | 2 | \([1, 2]\) | |
| ctype | integer | - | \([1, 2]\) |
References
Friedman J, Hastie T, Tibshirani R (2010). “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software, 33(1), 1–22. doi:10.18637/jss.v033.i01 .
See also
as.data.table(mlr_learners)for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner -> mlr3proba::LearnerSurv -> LearnerSurvGlmnet
Methods
Method selected_features()
Returns the set of selected features as reported by glmnet::predict.glmnet()
with type set to "nonzero".
Arguments
lambda(
numeric(1))
Customlambda, defaults to the active lambda depending on parameter set.
Returns
(character()) of feature names.
Examples
# Define the Learner
learner = mlr3::lrn("surv.glmnet")
print(learner)
#> <LearnerSurvGlmnet:surv.glmnet>: Regularized Generalized Linear Model
#> * Model: -
#> * Parameters: list()
#> * Packages: mlr3, mlr3proba, mlr3extralearners, glmnet
#> * Predict Types: [crank], distr, lp
#> * Feature Types: logical, integer, numeric
#> * Properties: selected_features, weights
# Define a Task
task = mlr3::tsk("grace")
# Create train and test set
ids = mlr3::partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
print(learner$model)
#> $model
#>
#> Call: (if (cv) glmnet::cv.glmnet else glmnet::glmnet)(x = data, y = target, family = "cox")
#>
#> Df %Dev Lambda
#> 1 0 0.00 0.201600
#> 2 1 0.67 0.183700
#> 3 2 1.31 0.167400
#> 4 2 2.30 0.152500
#> 5 2 3.15 0.138900
#> 6 3 5.36 0.126600
#> 7 3 7.97 0.115400
#> 8 3 9.94 0.105100
#> 9 3 11.47 0.095770
#> 10 3 12.70 0.087260
#> 11 4 13.74 0.079510
#> 12 4 14.62 0.072450
#> 13 4 15.35 0.066010
#> 14 4 15.96 0.060150
#> 15 4 16.48 0.054800
#> 16 4 16.92 0.049930
#> 17 4 17.29 0.045500
#> 18 4 17.60 0.041460
#> 19 4 17.87 0.037770
#> 20 4 18.09 0.034420
#> 21 5 18.29 0.031360
#> 22 6 18.48 0.028570
#> 23 6 18.64 0.026040
#> 24 6 18.78 0.023720
#> 25 6 18.90 0.021620
#> 26 6 19.00 0.019700
#> 27 6 19.08 0.017950
#> 28 6 19.15 0.016350
#> 29 6 19.21 0.014900
#> 30 6 19.26 0.013580
#> 31 6 19.30 0.012370
#> 32 6 19.34 0.011270
#> 33 6 19.36 0.010270
#> 34 6 19.39 0.009357
#> 35 6 19.41 0.008526
#> 36 6 19.43 0.007768
#> 37 6 19.44 0.007078
#> 38 6 19.45 0.006449
#> 39 6 19.46 0.005876
#> 40 6 19.47 0.005354
#> 41 6 19.48 0.004879
#> 42 6 19.49 0.004445
#> 43 6 19.49 0.004050
#> 44 6 19.50 0.003690
#> 45 6 19.50 0.003363
#>
#> $x
#> age los revasc revascdays stchange sysbp
#> [1,] 28 9 0 180 1 107
#> [2,] 35 5 1 2 0 172
#> [3,] 35 10 1 9 0 106
#> [4,] 35 2 0 180 0 121
#> [5,] 35 2 1 1 1 112
#> [6,] 37 9 0 180 1 151
#> [7,] 38 13 1 0 1 161
#> [8,] 36 1 0 180 1 155
#> [9,] 38 12 1 8 1 120
#> [10,] 36 5 1 0 1 115
#> [11,] 42 2 0 180 1 100
#> [12,] 38 5 1 3 0 125
#> [13,] 42 2 0 2 0 140
#> [14,] 40 6 0 180 1 138
#> [15,] 40 11 1 10 1 120
#> [16,] 42 2 0 180 0 100
#> [17,] 41 2 1 1 0 166
#> [18,] 40 1 1 0 1 145
#> [19,] 42 4 0 180 0 162
#> [20,] 42 15 1 13 1 125
#> [21,] 40 3 1 1 0 170
#> [22,] 43 2 1 1 1 116
#> [23,] 44 5 1 1 0 170
#> [24,] 41 10 1 8 0 150
#> [25,] 41 13 1 1 0 140
#> [26,] 45 6 0 180 1 170
#> [27,] 41 5 1 4 1 141
#> [28,] 44 2 1 1 1 150
#> [29,] 45 2 0 180 1 140
#> [30,] 46 15 0 180 0 120
#> [31,] 46 2 1 1 0 126
#> [32,] 48 15 0 180 1 160
#> [33,] 45 3 0 150 0 130
#> [34,] 46 7 1 2 0 166
#> [35,] 43 29 0 180 1 180
#> [36,] 45 4 1 0 0 124
#> [37,] 43 10 0 180 0 185
#> [38,] 47 6 1 0 1 116
#> [39,] 46 13 1 10 0 100
#> [40,] 44 0 1 0 1 96
#> [41,] 43 3 1 0 1 124
#> [42,] 45 8 1 0 1 117
#> [43,] 49 5 0 73 1 136
#> [44,] 45 5 0 5 0 141
#> [45,] 46 2 1 1 1 122
#> [46,] 46 6 1 0 1 100
#> [47,] 44 4 1 0 1 114
#> [48,] 47 2 0 180 0 108
#> [49,] 45 5 0 180 1 190
#> [50,] 46 5 1 3 0 130
#> [51,] 46 4 0 180 1 121
#> [52,] 45 9 1 0 1 145
#> [53,] 47 3 1 1 1 120
#> [54,] 48 3 0 180 0 154
#> [55,] 47 5 1 3 1 130
#> [56,] 47 9 1 6 0 170
#> [57,] 46 3 1 0 1 119
#> [58,] 49 4 0 180 0 117
#> [59,] 47 10 0 10 1 140
#> [60,] 50 1 1 0 1 129
#> [61,] 47 7 0 180 0 145
#> [62,] 50 4 1 1 0 125
#> [63,] 49 7 1 7 1 110
#> [64,] 46 9 1 9 1 122
#> [65,] 50 7 0 180 1 110
#> [66,] 51 1 0 1 1 145
#> [67,] 49 15 1 11 1 160
#> [68,] 49 23 0 179 1 112
#> [69,] 46 6 1 0 1 156
#> [70,] 50 4 0 4 1 100
#> [71,] 51 3 1 2 0 113
#> [72,] 50 1 1 0 0 150
#> [73,] 50 9 0 180 0 130
#> [74,] 47 8 0 180 0 160
#> [75,] 47 6 0 180 1 162
#> [76,] 51 8 0 180 1 140
#> [77,] 52 2 0 180 0 155
#> [78,] 46 3 0 180 1 120
#> [79,] 46 1 1 1 0 145
#> [80,] 50 4 1 1 0 150
#> [81,] 49 9 1 3 0 102
#> [82,] 53 5 0 180 1 140
#> [83,] 54 17 1 12 1 102
#> [84,] 54 6 1 3 0 129
#> [85,] 50 2 0 5 1 106
#> [86,] 49 5 1 2 1 150
#> [87,] 52 14 1 7 1 200
#> [88,] 48 11 1 10 0 120
#> [89,] 53 4 1 0 1 156
#> [90,] 54 9 1 0 1 138
#> [91,] 49 16 0 16 0 125
#> [92,] 54 23 1 10 0 131
#> [93,] 52 7 1 2 0 154
#> [94,] 55 6 1 2 1 114
#> [95,] 54 9 1 1 0 130
#> [96,] 55 4 1 2 0 150
#> [97,] 52 4 0 180 1 180
#> [98,] 51 13 1 11 0 145
#> [99,] 50 5 1 4 1 150
#> [100,] 54 4 1 0 1 121
#> [101,] 52 4 0 180 0 183
#> [102,] 50 3 0 174 1 153
#> [103,] 49 6 1 0 1 130
#> [104,] 49 1 0 1 1 110
#> [105,] 50 7 1 1 0 156
#> [106,] 53 8 1 0 1 130
#> [107,] 56 4 1 1 1 130
#> [108,] 52 5 0 175 1 117
#> [109,] 55 1 0 180 0 127
#> [110,] 55 2 0 2 0 145
#> [111,] 54 1 0 180 0 162
#> [112,] 54 7 1 0 1 100
#> [113,] 56 2 0 180 0 132
#> [114,] 54 3 0 180 1 180
#> [115,] 52 16 0 16 0 152
#> [116,] 52 16 1 14 0 170
#> [117,] 53 15 0 15 1 90
#> [118,] 55 6 0 180 1 100
#> [119,] 54 12 1 0 1 190
#> [120,] 55 2 0 134 1 140
#> [121,] 55 1 0 2 0 130
#> [122,] 57 3 0 3 0 120
#> [123,] 54 7 1 2 0 129
#> [124,] 54 2 1 1 0 135
#> [125,] 57 5 1 3 1 138
#> [126,] 56 4 1 0 1 140
#> [127,] 55 11 1 7 0 104
#> [128,] 52 15 1 14 0 130
#> [129,] 57 10 0 180 1 170
#> [130,] 58 8 0 8 1 130
#> [131,] 54 5 0 180 1 108
#> [132,] 53 21 1 13 1 130
#> [133,] 59 3 1 1 0 172
#> [134,] 57 4 0 180 1 119
#> [135,] 54 17 1 8 1 227
#> [136,] 55 9 1 2 1 147
#> [137,] 55 13 0 166 1 140
#> [138,] 57 4 1 2 1 185
#> [139,] 57 11 1 10 1 129
#> [140,] 55 3 1 2 0 140
#> [141,] 55 5 0 5 1 131
#> [142,] 54 7 1 0 1 141
#> [143,] 56 4 0 4 0 164
#> [144,] 59 15 1 10 0 140
#> [145,] 58 9 1 0 1 180
#> [146,] 55 2 0 2 0 106
#> [147,] 59 9 1 1 1 125
#> [148,] 57 1 0 180 0 148
#> [149,] 60 11 1 9 0 106
#> [150,] 59 3 0 180 0 120
#> [151,] 58 4 1 0 1 160
#> [152,] 57 2 0 2 1 120
#> [153,] 57 5 0 180 1 130
#> [154,] 55 5 1 0 1 160
#> [155,] 57 10 1 9 0 103
#> [156,] 59 5 0 180 1 155
#> [157,] 58 26 1 0 1 189
#> [158,] 61 9 0 9 1 160
#> [159,] 58 4 1 3 0 120
#> [160,] 58 8 0 161 1 140
#> [161,] 58 14 1 6 0 190
#> [162,] 61 4 1 3 0 151
#> [163,] 61 9 1 8 0 150
#> [164,] 61 3 1 2 1 102
#> [165,] 58 1 0 1 1 100
#> [166,] 57 13 1 10 0 110
#> [167,] 57 2 1 0 1 116
#> [168,] 58 10 0 10 1 150
#> [169,] 57 4 1 3 0 138
#> [170,] 57 11 0 180 1 150
#> [171,] 61 3 0 17 0 143
#> [172,] 57 3 1 2 0 120
#> [173,] 56 13 1 6 1 158
#> [174,] 59 9 1 0 1 80
#> [175,] 55 4 1 3 1 160
#> [176,] 58 11 0 172 1 135
#> [177,] 60 12 1 0 1 114
#> [178,] 55 9 1 7 1 135
#> [179,] 61 4 1 0 1 115
#> [180,] 59 11 1 8 1 190
#> [181,] 57 1 0 1 0 126
#> [182,] 59 5 1 2 0 182
#> [183,] 59 10 0 180 0 160
#> [184,] 61 13 0 13 0 210
#> [185,] 58 8 1 5 0 152
#> [186,] 62 10 1 0 1 153
#> [187,] 62 7 1 2 1 180
#> [188,] 57 3 1 0 0 100
#> [189,] 61 28 1 7 0 133
#> [190,] 58 8 1 3 1 150
#> [191,] 57 7 0 169 0 180
#> [192,] 61 7 0 7 1 150
#> [193,] 61 6 0 6 0 134
#> [194,] 59 13 1 2 0 198
#> [195,] 57 12 1 9 1 120
#> [196,] 60 17 1 8 1 140
#> [197,] 58 3 1 0 1 146
#> [198,] 62 4 1 3 0 173
#> [199,] 63 6 0 28 1 120
#> [200,] 57 18 1 9 1 93
#> [201,] 61 5 0 5 1 160
#> [202,] 57 2 1 1 0 159
#> [203,] 62 17 1 10 1 180
#> [204,] 62 1 1 0 1 172
#> [205,] 61 7 0 180 0 135
#> [206,] 63 4 1 3 0 222
#> [207,] 64 4 0 180 0 130
#> [208,] 63 4 1 1 0 155
#> [209,] 60 18 1 13 0 132
#> [210,] 58 9 1 9 0 110
#> [211,] 62 7 0 7 0 150
#> [212,] 59 1 0 22 1 162
#> [213,] 58 2 0 180 0 127
#> [214,] 59 4 0 180 0 196
#> [215,] 60 7 1 5 1 141
#> [216,] 60 7 1 1 1 90
#> [217,] 65 13 0 180 1 100
#> [218,] 63 1 0 1 0 162
#> [219,] 63 1 0 1 0 130
#> [220,] 62 6 0 180 0 170
#> [221,] 61 15 1 13 0 170
#> [222,] 64 10 1 9 0 160
#> [223,] 62 6 0 6 0 120
#> [224,] 60 8 0 17 1 130
#> [225,] 61 6 1 1 1 117
#> [226,] 64 12 1 11 0 160
#> [227,] 66 1 1 0 1 120
#> [228,] 64 6 1 0 1 140
#> [229,] 63 14 1 9 0 123
#> [230,] 65 36 1 11 0 140
#> [231,] 63 4 1 3 0 162
#> [232,] 66 3 1 1 0 127
#> [233,] 61 10 1 2 1 194
#> [234,] 63 12 1 9 0 114
#> [235,] 66 5 1 0 1 110
#> [236,] 65 10 1 8 1 120
#> [237,] 61 12 1 11 0 154
#> [238,] 65 3 0 180 1 190
#> [239,] 63 16 1 7 1 110
#> [240,] 66 6 1 1 1 130
#> [241,] 63 12 0 12 1 150
#> [242,] 65 6 0 9 0 112
#> [243,] 65 3 1 0 1 80
#> [244,] 63 2 1 1 0 180
#> [245,] 67 11 0 11 1 100
#> [246,] 64 2 0 2 0 201
#> [247,] 66 18 1 5 0 142
#> [248,] 66 16 1 11 1 169
#> [249,] 61 15 1 10 0 130
#> [250,] 63 9 1 8 1 160
#> [251,] 63 2 1 0 0 140
#> [252,] 64 19 1 8 1 160
#> [253,] 65 8 1 0 1 140
#> [254,] 67 6 0 180 1 170
#> [255,] 65 15 1 11 1 160
#> [256,] 68 5 1 4 1 150
#> [257,] 64 13 1 12 1 150
#> [258,] 64 6 1 0 1 125
#> [259,] 66 13 1 0 0 118
#> [260,] 65 3 0 3 0 105
#> [261,] 64 0 0 0 1 148
#> [262,] 66 3 1 0 1 135
#> [263,] 66 6 1 0 1 140
#> [264,] 65 2 1 1 1 170
#> [265,] 68 1 0 180 1 166
#> [266,] 67 8 1 1 1 130
#> [267,] 68 5 0 5 1 90
#> [268,] 63 10 0 16 1 160
#> [269,] 66 14 0 180 0 130
#> [270,] 68 18 0 180 1 260
#> [271,] 65 18 1 3 0 120
#> [272,] 63 10 0 18 1 130
#> [273,] 67 11 0 11 0 150
#> [274,] 68 14 0 79 0 172
#> [275,] 66 12 1 10 1 150
#> [276,] 66 11 1 0 0 100
#> [277,] 65 4 1 2 1 145
#> [278,] 66 15 1 13 1 160
#> [279,] 69 6 0 180 1 100
#> [280,] 66 9 1 8 0 130
#> [281,] 68 14 1 13 1 140
#> [282,] 67 1 0 180 1 160
#> [283,] 68 10 1 10 1 150
#> [284,] 67 7 1 4 1 130
#> [285,] 63 2 1 0 0 99
#> [286,] 67 2 0 180 0 184
#> [287,] 65 10 1 1 1 148
#> [288,] 69 6 0 99 1 140
#> [289,] 65 4 1 1 0 130
#> [290,] 70 15 1 12 1 132
#> [291,] 64 4 0 180 1 140
#> [292,] 66 4 0 180 0 177
#> [293,] 68 4 1 0 1 160
#> [294,] 69 4 1 3 1 150
#> [295,] 64 21 0 21 1 155
#> [296,] 66 6 0 180 0 140
#> [297,] 65 1 0 1 1 120
#> [298,] 65 6 0 101 1 115
#> [299,] 71 3 0 5 0 112
#> [300,] 71 20 1 0 1 160
#> [301,] 67 2 0 180 0 128
#> [302,] 66 9 1 3 1 151
#> [303,] 66 1 1 1 1 165
#> [304,] 70 4 1 0 1 180
#> [305,] 69 8 0 180 1 153
#> [306,] 70 14 0 171 0 166
#> [307,] 68 18 1 14 1 170
#> [308,] 69 0 0 0 1 148
#> [309,] 65 2 0 180 0 130
#> [310,] 68 7 1 0 1 150
#> [311,] 69 3 1 2 0 151
#> [312,] 69 8 0 180 1 180
#> [313,] 71 7 0 7 0 230
#> [314,] 66 2 0 2 1 228
#> [315,] 71 6 0 45 1 158
#> [316,] 69 5 0 5 1 142
#> [317,] 69 3 0 3 1 130
#> [318,] 70 22 1 13 0 103
#> [319,] 67 1 0 36 1 104
#> [320,] 68 6 0 180 0 145
#> [321,] 69 8 1 5 1 195
#> [322,] 69 6 1 4 1 174
#> [323,] 72 3 1 0 1 132
#> [324,] 69 8 1 7 1 108
#> [325,] 67 3 0 180 0 110
#> [326,] 66 2 1 1 0 123
#> [327,] 67 14 0 172 1 140
#> [328,] 69 11 1 0 1 120
#> [329,] 66 2 0 180 0 130
#> [330,] 69 4 1 3 0 132
#> [331,] 69 8 1 2 0 121
#> [332,] 67 13 1 9 0 130
#> [333,] 70 3 0 123 0 130
#> [334,] 70 9 0 180 1 142
#> [335,] 72 5 1 4 0 170
#> [336,] 67 22 1 1 1 140
#> [337,] 68 3 0 19 0 135
#> [338,] 67 12 1 8 0 120
#> [339,] 69 1 0 1 1 110
#> [340,] 67 1 0 1 1 60
#> [341,] 69 5 0 76 0 120
#> [342,] 67 8 1 0 1 130
#> [343,] 68 10 1 8 1 160
#> [344,] 70 35 1 0 1 105
#> [345,] 72 30 1 0 1 145
#> [346,] 70 7 0 7 0 102
#> [347,] 68 7 1 2 0 135
#> [348,] 71 6 0 9 0 120
#> [349,] 69 10 1 6 1 120
#> [350,] 70 11 0 180 1 210
#> [351,] 72 19 1 8 0 120
#> [352,] 72 12 1 10 0 170
#> [353,] 67 5 1 0 1 147
#> [354,] 67 9 0 180 0 158
#> [355,] 73 13 0 152 1 130
#> [356,] 70 5 0 180 0 150
#> [357,] 67 4 1 1 0 134
#> [358,] 71 1 0 173 1 188
#> [359,] 68 23 0 180 1 220
#> [360,] 70 3 0 180 0 121
#> [361,] 71 3 1 2 0 150
#> [362,] 68 4 1 3 0 210
#> [363,] 72 5 0 28 0 120
#> [364,] 71 5 0 180 0 191
#> [365,] 69 16 1 10 1 140
#> [366,] 72 16 1 1 1 130
#> [367,] 69 1 1 0 0 155
#> [368,] 72 8 1 1 1 150
#> [369,] 71 2 1 0 1 180
#> [370,] 73 7 0 7 1 140
#> [371,] 68 15 1 13 1 130
#> [372,] 70 3 0 3 1 159
#> [373,] 70 13 1 9 0 100
#> [374,] 73 0 0 180 1 161
#> [375,] 71 3 1 1 0 150
#> [376,] 71 15 1 11 0 165
#> [377,] 74 20 0 20 1 180
#> [378,] 68 9 0 180 1 120
#> [379,] 73 3 1 0 1 136
#> [380,] 70 5 1 0 1 190
#> [381,] 71 17 1 11 0 160
#> [382,] 71 8 1 7 0 149
#> [383,] 73 10 1 8 0 106
#> [384,] 69 12 1 1 1 149
#> [385,] 74 4 0 4 0 120
#> [386,] 73 4 0 58 1 160
#> [387,] 72 5 1 3 1 160
#> [388,] 70 3 0 180 1 154
#> [389,] 73 6 0 180 0 110
#> [390,] 72 8 1 0 1 140
#> [391,] 74 3 0 3 1 150
#> [392,] 70 4 1 0 1 140
#> [393,] 71 14 1 13 1 170
#> [394,] 74 7 1 0 1 117
#> [395,] 72 10 1 8 1 153
#> [396,] 69 7 0 180 1 144
#> [397,] 70 8 0 8 0 120
#> [398,] 71 10 1 9 1 120
#> [399,] 75 1 0 1 0 133
#> [400,] 75 2 1 1 0 145
#> [401,] 73 10 1 9 1 146
#> [402,] 73 10 1 10 1 120
#> [403,] 74 15 1 9 1 179
#> [404,] 71 2 0 10 1 112
#> [405,] 73 1 0 1 1 80
#> [406,] 75 9 1 7 0 140
#> [407,] 75 13 1 1 1 130
#> [408,] 71 4 0 4 0 134
#> [409,] 73 10 1 8 0 120
#> [410,] 70 7 1 4 0 184
#> [411,] 72 1 1 1 0 168
#> [412,] 72 7 0 57 1 145
#> [413,] 73 10 0 180 0 162
#> [414,] 70 3 0 3 0 150
#> [415,] 76 25 1 12 1 170
#> [416,] 73 12 1 12 1 140
#> [417,] 72 2 0 180 0 120
#> [418,] 72 4 1 0 1 197
#> [419,] 71 3 1 0 0 144
#> [420,] 73 5 0 180 0 126
#> [421,] 73 4 0 180 0 124
#> [422,] 74 34 1 8 1 233
#> [423,] 76 3 1 0 1 120
#> [424,] 72 5 0 180 0 154
#> [425,] 77 4 0 4 0 185
#> [426,] 75 3 1 1 0 180
#> [427,] 72 7 1 2 0 142
#> [428,] 71 16 0 180 0 140
#> [429,] 73 10 1 10 0 124
#> [430,] 74 7 0 180 1 150
#> [431,] 74 3 0 3 1 128
#> [432,] 76 1 0 180 0 114
#> [433,] 74 2 1 1 0 140
#> [434,] 76 8 1 0 1 141
#> [435,] 74 19 1 4 1 200
#> [436,] 73 6 0 6 1 114
#> [437,] 74 2 0 180 0 190
#> [438,] 72 4 1 3 0 160
#> [439,] 76 17 1 0 1 200
#> [440,] 73 4 1 3 1 125
#> [441,] 75 7 0 7 0 190
#> [442,] 75 0 0 0 1 130
#> [443,] 73 13 1 11 0 195
#> [444,] 75 12 0 12 1 160
#> [445,] 76 13 1 8 1 148
#> [446,] 75 4 1 2 1 188
#> [447,] 76 4 0 4 1 155
#> [448,] 75 1 0 1 1 125
#> [449,] 74 2 0 180 0 111
#> [450,] 73 1 0 52 1 105
#> [451,] 72 5 0 180 0 120
#> [452,] 76 44 1 10 0 105
#> [453,] 74 10 1 0 1 135
#> [454,] 76 5 1 0 1 167
#> [455,] 74 8 1 8 1 170
#> [456,] 75 9 0 180 1 140
#> [457,] 73 33 1 12 1 175
#> [458,] 77 5 1 0 0 123
#> [459,] 77 12 1 9 1 100
#> [460,] 73 10 1 9 0 146
#> [461,] 76 12 1 11 1 120
#> [462,] 73 7 1 0 0 174
#> [463,] 74 6 0 79 1 140
#> [464,] 75 3 1 1 1 171
#> [465,] 75 6 0 180 0 150
#> [466,] 79 10 1 8 0 190
#> [467,] 74 2 1 0 1 130
#> [468,] 78 18 0 18 1 144
#> [469,] 77 3 0 180 0 110
#> [470,] 76 29 0 47 0 90
#> [471,] 73 11 1 2 1 110
#> [472,] 78 7 0 7 1 133
#> [473,] 74 15 0 180 1 172
#> [474,] 76 13 1 1 1 170
#> [475,] 78 32 1 9 1 198
#> [476,] 80 10 1 6 1 147
#> [477,] 78 0 0 180 1 212
#> [478,] 78 13 1 5 0 130
#> [479,] 75 5 0 119 1 150
#> [480,] 75 12 1 1 1 120
#> [481,] 78 15 0 180 1 270
#> [482,] 80 8 0 8 1 120
#> [483,] 75 13 1 6 0 150
#> [484,] 76 1 0 1 1 83
#> [485,] 79 4 0 80 0 145
#> [486,] 78 2 1 1 0 130
#> [487,] 75 4 1 0 0 212
#> [488,] 78 10 0 180 1 130
#> [489,] 76 11 1 0 0 120
#> [490,] 75 11 1 4 0 162
#> [491,] 75 3 0 3 0 0
#> [492,] 77 24 0 24 1 160
#> [493,] 79 8 0 32 1 120
#> [494,] 80 9 0 23 1 128
#> [495,] 78 6 1 0 1 240
#> [496,] 78 11 1 1 1 140
#> [497,] 78 11 1 8 1 118
#> [498,] 79 4 0 4 1 125
#> [499,] 76 12 1 10 1 127
#> [500,] 77 6 0 6 1 107
#> [501,] 80 3 1 0 1 120
#> [502,] 75 2 1 1 1 204
#> [503,] 78 11 0 180 1 135
#> [504,] 76 1 0 1 1 140
#> [505,] 76 1 0 1 1 90
#> [506,] 78 7 1 0 1 110
#> [507,] 79 3 0 3 0 120
#> [508,] 77 7 0 180 1 170
#> [509,] 77 6 0 6 1 144
#> [510,] 79 4 1 0 1 120
#> [511,] 81 1 0 180 0 120
#> [512,] 80 15 1 12 1 150
#> [513,] 78 4 0 59 1 112
#> [514,] 80 17 1 12 0 100
#> [515,] 76 7 0 161 0 151
#> [516,] 79 10 0 10 1 120
#> [517,] 81 4 1 2 1 126
#> [518,] 79 28 0 164 0 100
#> [519,] 80 9 0 118 1 186
#> [520,] 80 6 0 173 1 160
#> [521,] 78 32 0 180 1 130
#> [522,] 81 3 0 180 0 184
#> [523,] 81 2 0 175 0 172
#> [524,] 78 7 0 7 1 147
#> [525,] 78 15 0 15 0 165
#> [526,] 80 5 1 1 1 108
#> [527,] 78 4 0 180 0 175
#> [528,] 79 3 0 3 1 101
#> [529,] 78 26 1 5 0 194
#> [530,] 76 1 0 166 0 131
#> [531,] 80 1 0 1 0 100
#> [532,] 78 3 1 1 1 152
#> [533,] 82 3 1 1 1 144
#> [534,] 77 5 0 85 0 188
#> [535,] 79 6 0 6 0 152
#> [536,] 78 2 0 180 0 148
#> [537,] 80 5 0 5 1 130
#> [538,] 82 1 1 0 1 82
#> [539,] 79 10 0 180 1 150
#> [540,] 77 4 0 180 1 98
#> [541,] 81 1 0 108 0 129
#> [542,] 78 12 0 180 0 134
#> [543,] 80 6 0 6 1 110
#> [544,] 82 5 0 180 0 110
#> [545,] 79 7 1 6 0 130
#> [546,] 83 4 0 103 0 97
#> [547,] 80 11 1 8 0 170
#> [548,] 78 23 1 10 1 145
#> [549,] 78 9 1 4 1 120
#> [550,] 82 8 1 1 0 128
#> [551,] 79 1 0 180 1 170
#> [552,] 81 15 0 180 1 140
#> [553,] 80 7 1 0 1 146
#> [554,] 81 16 0 16 1 110
#> [555,] 81 8 0 180 0 146
#> [556,] 79 7 0 177 0 197
#> [557,] 85 4 0 180 0 90
#> [558,] 83 2 0 2 1 155
#> [559,] 82 6 0 128 1 100
#> [560,] 84 4 0 167 0 198
#> [561,] 80 3 1 1 1 230
#> [562,] 82 23 1 0 0 110
#> [563,] 84 4 0 4 1 85
#> [564,] 81 1 0 1 1 150
#> [565,] 84 1 0 38 1 205
#> [566,] 81 4 0 90 1 138
#> [567,] 80 13 1 8 1 140
#> [568,] 84 4 0 89 1 129
#> [569,] 80 2 1 0 1 130
#> [570,] 79 4 0 4 1 60
#> [571,] 83 1 0 1 1 100
#> [572,] 82 19 0 19 0 120
#> [573,] 83 9 0 180 0 198
#> [574,] 79 14 1 0 0 110
#> [575,] 83 3 0 114 0 98
#> [576,] 81 14 1 12 1 128
#> [577,] 83 2 0 154 0 130
#> [578,] 83 1 0 180 0 160
#> [579,] 81 4 0 4 0 175
#> [580,] 84 15 1 13 1 110
#> [581,] 81 1 0 1 1 145
#> [582,] 81 12 0 12 1 163
#> [583,] 82 5 1 0 1 146
#> [584,] 81 4 0 4 0 160
#> [585,] 86 12 0 180 1 120
#> [586,] 83 12 1 2 1 170
#> [587,] 81 19 1 14 0 120
#> [588,] 82 3 1 2 0 130
#> [589,] 82 15 1 0 0 183
#> [590,] 83 7 0 126 0 135
#> [591,] 86 8 0 8 1 132
#> [592,] 84 6 0 165 0 145
#> [593,] 86 3 0 3 1 140
#> [594,] 82 9 0 180 1 134
#> [595,] 84 3 0 180 1 120
#> [596,] 81 13 0 180 0 152
#> [597,] 85 3 0 3 1 118
#> [598,] 82 1 0 180 1 193
#> [599,] 83 4 0 4 0 130
#> [600,] 86 12 1 0 1 132
#> [601,] 82 14 1 11 1 103
#> [602,] 86 6 1 0 1 140
#> [603,] 84 16 0 70 1 150
#> [604,] 83 19 0 43 0 150
#> [605,] 84 3 1 2 0 125
#> [606,] 83 10 1 0 1 190
#> [607,] 86 2 0 180 1 169
#> [608,] 84 3 0 3 1 121
#> [609,] 83 13 1 12 0 170
#> [610,] 84 9 0 92 1 110
#> [611,] 84 3 0 180 1 170
#> [612,] 86 4 0 38 1 122
#> [613,] 82 4 0 4 0 130
#> [614,] 86 13 0 177 0 163
#> [615,] 86 6 0 6 1 117
#> [616,] 88 4 0 4 0 100
#> [617,] 85 22 0 22 1 184
#> [618,] 83 9 0 65 1 150
#> [619,] 86 9 1 7 1 142
#> [620,] 87 2 0 180 1 130
#> [621,] 86 6 0 46 0 173
#> [622,] 88 3 0 115 0 110
#> [623,] 88 2 0 180 1 68
#> [624,] 87 8 0 8 1 157
#> [625,] 86 15 1 8 1 109
#> [626,] 88 4 0 4 0 86
#> [627,] 89 4 0 4 1 153
#> [628,] 89 5 0 119 1 140
#> [629,] 87 6 0 180 1 110
#> [630,] 87 1 0 1 0 170
#> [631,] 84 2 0 110 1 174
#> [632,] 87 29 0 29 1 97
#> [633,] 84 0 0 180 1 136
#> [634,] 88 1 0 1 0 135
#> [635,] 86 4 0 180 1 145
#> [636,] 91 8 0 8 0 100
#> [637,] 87 2 0 180 0 160
#> [638,] 87 6 1 0 0 125
#> [639,] 88 8 0 50 1 154
#> [640,] 87 6 0 126 1 168
#> [641,] 86 9 1 7 0 130
#> [642,] 90 4 1 0 0 121
#> [643,] 91 1 0 1 1 74
#> [644,] 90 5 1 0 1 125
#> [645,] 89 3 1 1 1 160
#> [646,] 92 1 0 1 1 167
#> [647,] 91 3 0 33 1 137
#> [648,] 88 5 0 158 0 100
#> [649,] 87 7 0 74 1 105
#> [650,] 89 2 0 168 0 118
#> [651,] 91 5 0 169 1 176
#> [652,] 92 7 0 7 1 110
#> [653,] 89 4 0 4 1 159
#> [654,] 91 0 0 0 0 0
#> [655,] 89 14 0 180 1 84
#> [656,] 90 18 0 180 0 188
#> [657,] 91 4 1 0 1 120
#> [658,] 94 6 0 50 0 78
#> [659,] 93 8 0 179 1 110
#> [660,] 94 8 0 8 1 142
#> [661,] 91 1 0 180 0 158
#> [662,] 90 16 0 16 1 106
#> [663,] 95 8 1 5 1 150
#> [664,] 91 12 0 53 1 212
#> [665,] 91 7 0 7 0 135
#> [666,] 93 0 1 0 1 122
#> [667,] 92 5 0 69 0 139
#> [668,] 92 2 0 2 0 112
#> [669,] 96 3 1 0 1 104
#> [670,] 96 15 1 0 1 140
#>
#> $y
#> [1] 180.0+ 5.0+ 180.0+ 180.0+ 2.0+ 180.0+ 180.0+ 180.0+ 12.0 5.0+
#> [11] 180.0+ 5.0+ 2.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [21] 180.0+ 2.0+ 155.0+ 180.0+ 180.0+ 180.0+ 5.0+ 180.0+ 180.0+ 180.0+
#> [31] 180.0+ 180.0+ 150.0 180.0+ 180.0+ 180.0+ 180.0+ 6.0+ 180.0+ 180.0+
#> [41] 180.0+ 180.0+ 73.0 5.0+ 161.0+ 180.0+ 180.0+ 180.0+ 180.0+ 5.0+
#> [51] 180.0+ 177.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 10.0+ 172.0+
#> [61] 180.0+ 180.0+ 7.0 180.0+ 180.0+ 1.0 179.0+ 179.0+ 180.0+ 4.0+
#> [71] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [81] 180.0+ 180.0+ 180.0+ 180.0+ 5.0 180.0+ 85.0 180.0+ 166.0+ 180.0+
#> [91] 16.0+ 152.0+ 7.0+ 6.0+ 180.0+ 180.0+ 180.0+ 13.0+ 171.0+ 180.0+
#> [101] 180.0+ 174.0+ 6.0+ 1.0 180.0+ 180.0+ 180.0+ 175.0+ 180.0+ 2.0
#> [111] 180.0+ 7.0+ 180.0+ 180.0+ 16.0+ 16.0 15.0+ 180.0+ 12.0+ 134.0+
#> [121] 2.0 3.0+ 180.0+ 180.0+ 140.0 165.0 180.0+ 180.0+ 180.0+ 8.0+
#> [131] 180.0+ 180.0+ 180.0+ 180.0+ 171.0+ 15.0 166.0+ 4.0+ 180.0+ 180.0+
#> [141] 5.0+ 180.0+ 4.0+ 180.0+ 9.0+ 2.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [151] 180.0+ 2.0 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 9.0+ 180.0+ 161.0+
#> [161] 171.0+ 180.0+ 180.0+ 3.0 1.0 180.0+ 180.0+ 10.0+ 180.0+ 180.0+
#> [171] 17.0 3.0+ 180.0+ 9.0+ 180.0+ 172.0+ 172.0+ 24.0 180.0+ 180.0+
#> [181] 1.0+ 180.0+ 180.0+ 13.0+ 8.0+ 180.0+ 180.0+ 180.0+ 94.0 180.0+
#> [191] 169.0 7.0 6.0 180.0+ 180.0+ 180.0+ 3.0+ 180.0+ 28.0 18.0
#> [201] 5.0+ 180.0+ 180.0+ 1.0 180.0+ 180.0+ 180.0+ 4.0+ 180.0+ 9.0
#> [211] 7.0+ 22.0 180.0+ 180.0+ 84.0 180.0+ 180.0+ 1.0 1.0 180.0+
#> [221] 180.0+ 167.0 6.0+ 17.0 180.0+ 12.0 180.0+ 180.0+ 14.0+ 36.0
#> [231] 180.0+ 3.0+ 88.0 12.0 180.0+ 180.0+ 12.0+ 180.0+ 180.0+ 180.0+
#> [241] 12.0 9.0 3.0 180.0+ 11.0+ 2.0+ 18.0+ 180.0+ 180.0+ 180.0+
#> [251] 2.0+ 103.0 15.0 180.0+ 180.0+ 5.0+ 13.0 180.0+ 166.0+ 3.0
#> [261] 0.5+ 3.0+ 180.0+ 175.0+ 180.0+ 8.0 5.0 16.0 180.0+ 180.0+
#> [271] 123.0+ 18.0 11.0+ 79.0 80.0 180.0+ 4.0+ 180.0+ 180.0+ 180.0+
#> [281] 180.0+ 180.0+ 10.0 180.0+ 180.0+ 180.0+ 180.0+ 99.0 180.0+ 180.0+
#> [291] 180.0+ 180.0+ 180.0+ 152.0+ 21.0+ 180.0+ 1.0 101.0 5.0 180.0+
#> [301] 180.0+ 180.0+ 1.0 180.0+ 180.0+ 171.0 180.0+ 0.5 180.0+ 180.0+
#> [311] 180.0+ 180.0+ 7.0+ 2.0 45.0 5.0+ 3.0+ 180.0+ 36.0 180.0+
#> [321] 180.0+ 97.0 180.0+ 8.0+ 180.0+ 2.0+ 172.0+ 180.0+ 180.0+ 180.0+
#> [331] 8.0+ 13.0+ 123.0 180.0+ 180.0+ 51.0 19.0 180.0+ 1.0 1.0
#> [341] 76.0 180.0+ 10.0+ 180.0+ 162.0 7.0+ 7.0+ 9.0 180.0+ 180.0+
#> [351] 180.0+ 12.0 180.0+ 180.0+ 152.0 180.0+ 76.0 173.0+ 180.0+ 180.0+
#> [361] 180.0+ 180.0+ 28.0 180.0+ 16.0+ 16.0+ 180.0+ 180.0+ 180.0+ 7.0+
#> [371] 15.0 3.0+ 13.0+ 180.0+ 3.0+ 180.0+ 20.0 180.0+ 180.0+ 180.0+
#> [381] 180.0+ 8.0 87.0 12.0 4.0+ 58.0 180.0+ 180.0+ 180.0+ 180.0+
#> [391] 3.0 180.0+ 14.0+ 180.0+ 10.0+ 180.0+ 8.0+ 179.0+ 1.0 180.0+
#> [401] 180.0+ 15.0 180.0+ 10.0 1.0 180.0+ 13.0 4.0+ 10.0 104.0+
#> [411] 1.0 57.0 180.0+ 3.0+ 180.0+ 12.0 180.0+ 180.0+ 180.0+ 180.0+
#> [421] 180.0+ 34.0 180.0+ 180.0+ 4.0+ 180.0+ 7.0 180.0+ 10.0 180.0+
#> [431] 3.0 180.0+ 180.0+ 180.0+ 180.0+ 6.0 180.0+ 180.0+ 17.0+ 180.0+
#> [441] 7.0 0.5 180.0+ 12.0 180.0+ 46.0 4.0 1.0 180.0+ 52.0
#> [451] 180.0+ 180.0+ 180.0+ 180.0+ 8.0 180.0+ 33.0 5.0 180.0+ 180.0+
#> [461] 12.0 7.0+ 79.0 3.0 180.0+ 180.0+ 176.0+ 18.0 180.0+ 47.0
#> [471] 11.0 7.0 180.0+ 180.0+ 32.0 10.0 180.0+ 172.0 119.0 12.0
#> [481] 180.0+ 8.0 180.0+ 1.0 80.0 180.0+ 4.0+ 180.0+ 11.0 152.0+
#> [491] 3.0 24.0 32.0 23.0 180.0+ 180.0+ 11.0 4.0 180.0+ 6.0
#> [501] 3.0+ 2.0+ 180.0+ 1.0 1.0 43.0 3.0 180.0+ 6.0 138.0
#> [511] 180.0+ 180.0+ 59.0 17.0 161.0 10.0+ 93.0 164.0 118.0 173.0
#> [521] 180.0+ 180.0+ 175.0+ 7.0+ 15.0+ 5.0+ 180.0+ 3.0 171.0+ 166.0+
#> [531] 1.0 3.0+ 180.0+ 85.0 6.0+ 180.0+ 5.0 1.0 180.0+ 180.0+
#> [541] 108.0 180.0+ 6.0 180.0+ 180.0+ 103.0 169.0 70.0 180.0+ 180.0+
#> [551] 180.0+ 180.0+ 7.0+ 16.0 180.0+ 177.0+ 180.0+ 2.0 128.0 167.0
#> [561] 3.0+ 62.0 4.0 1.0 38.0 90.0 180.0+ 89.0 180.0+ 4.0
#> [571] 1.0 19.0 180.0+ 180.0+ 114.0 180.0+ 154.0 180.0+ 4.0+ 180.0+
#> [581] 1.0 12.0 5.0+ 4.0+ 180.0+ 77.0 180.0+ 3.0 83.0 126.0
#> [591] 8.0 165.0 3.0 180.0+ 180.0+ 180.0+ 3.0+ 180.0+ 4.0+ 180.0+
#> [601] 174.0 6.0 70.0 43.0 180.0+ 180.0+ 180.0+ 3.0 13.0 92.0
#> [611] 180.0+ 38.0 4.0 177.0 6.0+ 4.0+ 22.0 65.0 11.0 180.0+
#> [621] 46.0 115.0 180.0+ 8.0+ 180.0+ 4.0 4.0 119.0 180.0+ 1.0+
#> [631] 110.0 29.0 180.0+ 1.0+ 180.0+ 8.0 180.0+ 25.0 50.0 126.0
#> [641] 180.0+ 4.0 1.0 89.0 3.0+ 1.0 33.0 158.0 74.0 168.0
#> [651] 169.0 7.0 4.0 0.5 180.0+ 180.0+ 4.0 50.0 179.0+ 8.0+
#> [661] 180.0+ 16.0 8.0 53.0 7.0+ 0.5 69.0 2.0 3.0 15.0+
#>
#> $weights
#> NULL
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
#> Warning: Multiple lambdas have been fit. Lambda will be set to 0.01 (see parameter 's').
# Score the predictions
predictions$score()
#> surv.cindex
#> 0.8290126