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 0.184100
#> 2 1 0.61 0.167800
#> 3 2 1.39 0.152900
#> 4 2 2.23 0.139300
#> 5 2 2.96 0.126900
#> 6 3 5.86 0.115600
#> 7 3 8.06 0.105400
#> 8 3 9.73 0.096000
#> 9 3 11.04 0.087470
#> 10 3 12.09 0.079700
#> 11 3 12.94 0.072620
#> 12 3 13.64 0.066170
#> 13 3 14.23 0.060290
#> 14 3 14.72 0.054930
#> 15 3 15.13 0.050050
#> 16 4 15.48 0.045610
#> 17 5 15.80 0.041560
#> 18 5 16.09 0.037860
#> 19 5 16.33 0.034500
#> 20 6 16.53 0.031440
#> 21 6 16.71 0.028640
#> 22 6 16.86 0.026100
#> 23 6 16.99 0.023780
#> 24 6 17.09 0.021670
#> 25 6 17.18 0.019740
#> 26 6 17.26 0.017990
#> 27 6 17.32 0.016390
#> 28 6 17.37 0.014930
#> 29 6 17.42 0.013610
#> 30 6 17.46 0.012400
#> 31 6 17.49 0.011300
#> 32 6 17.51 0.010290
#> 33 6 17.54 0.009379
#> 34 6 17.55 0.008546
#> 35 6 17.57 0.007787
#> 36 6 17.58 0.007095
#> 37 6 17.59 0.006465
#> 38 6 17.60 0.005890
#> 39 6 17.61 0.005367
#> 40 6 17.62 0.004890
#> 41 6 17.62 0.004456
#> 42 6 17.62 0.004060
#> 43 6 17.63 0.003699
#> 44 6 17.63 0.003371
#>
#> $x
#> age los revasc revascdays stchange sysbp
#> [1,] 32 5 1 0 1 121
#> [2,] 33 2 0 2 0 150
#> [3,] 35 5 1 2 0 172
#> [4,] 35 10 1 9 0 106
#> [5,] 34 5 0 5 0 120
#> [6,] 35 2 0 180 0 121
#> [7,] 35 2 1 1 1 112
#> [8,] 38 13 1 0 1 161
#> [9,] 38 2 0 115 0 150
#> [10,] 36 1 0 180 1 155
#> [11,] 35 0 0 180 1 119
#> [12,] 38 12 1 8 1 120
#> [13,] 36 5 1 0 1 115
#> [14,] 38 12 1 11 1 92
#> [15,] 40 12 1 9 0 153
#> [16,] 37 1 1 0 1 146
#> [17,] 40 2 1 1 1 148
#> [18,] 38 5 1 3 0 125
#> [19,] 42 2 0 2 0 140
#> [20,] 40 6 0 180 1 138
#> [21,] 42 2 0 180 0 100
#> [22,] 43 3 1 0 1 100
#> [23,] 41 2 1 1 0 166
#> [24,] 40 1 1 0 1 145
#> [25,] 43 4 1 0 1 130
#> [26,] 42 4 0 180 0 162
#> [27,] 42 15 1 13 1 125
#> [28,] 45 3 0 180 1 154
#> [29,] 44 3 0 180 0 141
#> [30,] 41 13 1 1 0 140
#> [31,] 45 9 1 7 0 110
#> [32,] 45 6 0 180 1 170
#> [33,] 41 5 1 4 1 141
#> [34,] 44 2 1 1 1 150
#> [35,] 43 2 0 180 1 140
#> [36,] 45 2 0 180 1 140
#> [37,] 46 15 0 180 0 120
#> [38,] 46 2 1 1 0 126
#> [39,] 47 4 1 3 0 118
#> [40,] 48 15 0 180 1 160
#> [41,] 45 3 0 150 0 130
#> [42,] 44 3 1 0 1 180
#> [43,] 46 7 1 2 0 166
#> [44,] 43 29 0 180 1 180
#> [45,] 43 10 0 180 0 185
#> [46,] 47 6 1 0 1 116
#> [47,] 46 13 1 10 0 100
#> [48,] 47 4 1 3 1 160
#> [49,] 43 3 1 0 1 124
#> [50,] 45 8 1 0 1 117
#> [51,] 49 5 0 73 1 136
#> [52,] 45 5 0 5 0 141
#> [53,] 44 4 1 0 1 114
#> [54,] 47 2 0 180 0 108
#> [55,] 45 5 0 180 1 190
#> [56,] 46 5 1 3 0 130
#> [57,] 46 15 0 180 1 120
#> [58,] 45 9 1 0 1 145
#> [59,] 47 3 1 1 1 120
#> [60,] 48 3 0 180 0 154
#> [61,] 48 12 1 11 0 200
#> [62,] 47 5 1 3 1 130
#> [63,] 47 9 1 6 0 170
#> [64,] 46 3 1 0 1 119
#> [65,] 49 4 0 180 0 117
#> [66,] 47 10 0 10 1 140
#> [67,] 50 1 1 0 1 129
#> [68,] 47 7 0 180 0 145
#> [69,] 50 4 1 1 0 125
#> [70,] 46 9 1 9 1 122
#> [71,] 50 7 0 180 1 110
#> [72,] 47 2 0 180 0 150
#> [73,] 46 6 1 0 1 156
#> [74,] 51 3 1 2 0 113
#> [75,] 50 1 1 0 0 150
#> [76,] 50 9 0 180 0 130
#> [77,] 49 7 1 4 1 90
#> [78,] 47 6 0 180 1 162
#> [79,] 51 8 0 180 1 140
#> [80,] 52 2 0 180 0 155
#> [81,] 46 3 0 180 1 120
#> [82,] 46 1 1 1 0 145
#> [83,] 48 7 1 0 1 110
#> [84,] 48 17 1 10 0 111
#> [85,] 47 2 1 1 0 110
#> [86,] 49 9 1 3 0 102
#> [87,] 49 15 0 180 1 160
#> [88,] 53 5 0 180 1 140
#> [89,] 54 17 1 12 1 102
#> [90,] 53 5 0 77 0 159
#> [91,] 53 7 1 0 0 199
#> [92,] 50 2 0 5 1 106
#> [93,] 50 10 1 6 0 122
#> [94,] 50 14 1 13 0 170
#> [95,] 48 3 1 2 0 150
#> [96,] 51 25 1 1 0 202
#> [97,] 53 4 0 4 0 140
#> [98,] 52 14 1 7 1 200
#> [99,] 48 11 1 10 0 120
#> [100,] 51 13 0 99 1 160
#> [101,] 49 16 0 16 0 125
#> [102,] 55 3 1 1 0 150
#> [103,] 54 23 1 10 0 131
#> [104,] 52 7 1 2 0 154
#> [105,] 55 6 1 2 1 114
#> [106,] 55 4 1 2 0 150
#> [107,] 52 4 0 180 1 180
#> [108,] 50 5 1 4 1 150
#> [109,] 54 4 1 0 1 121
#> [110,] 52 4 0 180 0 183
#> [111,] 55 28 1 13 1 160
#> [112,] 49 6 1 0 1 130
#> [113,] 49 1 0 1 1 110
#> [114,] 53 9 0 9 1 95
#> [115,] 53 8 1 0 1 130
#> [116,] 50 7 1 0 1 127
#> [117,] 56 4 1 1 1 130
#> [118,] 55 1 0 180 0 127
#> [119,] 54 7 1 0 1 100
#> [120,] 56 2 0 180 0 132
#> [121,] 54 3 0 180 1 180
#> [122,] 55 6 0 180 0 170
#> [123,] 53 10 1 9 0 172
#> [124,] 52 16 1 14 0 170
#> [125,] 55 6 0 180 1 100
#> [126,] 55 6 1 5 1 138
#> [127,] 54 12 1 0 1 190
#> [128,] 55 2 0 134 1 140
#> [129,] 55 1 0 2 0 130
#> [130,] 54 7 1 2 0 129
#> [131,] 54 2 1 1 0 135
#> [132,] 52 9 1 3 0 170
#> [133,] 54 2 1 1 1 176
#> [134,] 57 1 0 180 1 156
#> [135,] 56 4 1 0 1 140
#> [136,] 52 15 1 14 0 130
#> [137,] 53 3 1 0 1 200
#> [138,] 57 10 0 180 1 170
#> [139,] 58 8 0 8 1 130
#> [140,] 54 5 0 180 1 108
#> [141,] 55 3 1 1 1 156
#> [142,] 57 0 0 0 1 150
#> [143,] 53 21 1 13 1 130
#> [144,] 59 3 1 1 0 172
#> [145,] 57 4 0 180 1 119
#> [146,] 58 6 1 0 1 90
#> [147,] 54 17 1 8 1 227
#> [148,] 55 9 1 2 1 147
#> [149,] 55 13 0 166 1 140
#> [150,] 56 5 0 5 1 150
#> [151,] 57 4 1 2 1 185
#> [152,] 53 7 1 0 1 120
#> [153,] 57 11 1 10 1 129
#> [154,] 55 3 1 2 0 140
#> [155,] 54 7 1 0 1 141
#> [156,] 56 4 0 4 0 164
#> [157,] 58 9 1 0 1 180
#> [158,] 58 1 1 1 1 200
#> [159,] 55 5 1 0 0 140
#> [160,] 56 7 1 5 1 120
#> [161,] 55 2 0 2 0 106
#> [162,] 59 9 1 1 1 125
#> [163,] 57 1 0 180 0 148
#> [164,] 60 11 1 9 0 106
#> [165,] 59 3 0 180 0 120
#> [166,] 58 11 1 9 1 124
#> [167,] 55 5 1 0 1 160
#> [168,] 59 5 0 180 1 155
#> [169,] 59 4 1 0 1 152
#> [170,] 58 4 1 3 0 120
#> [171,] 59 2 1 1 0 140
#> [172,] 58 8 0 161 1 140
#> [173,] 61 9 1 8 0 150
#> [174,] 61 3 1 2 1 102
#> [175,] 58 1 0 1 1 100
#> [176,] 57 2 1 0 1 116
#> [177,] 58 10 0 10 1 150
#> [178,] 56 14 0 45 0 130
#> [179,] 57 3 1 2 0 120
#> [180,] 58 19 1 13 1 140
#> [181,] 56 18 1 11 1 165
#> [182,] 59 9 1 0 1 80
#> [183,] 58 11 0 172 1 135
#> [184,] 60 12 1 0 1 114
#> [185,] 55 9 1 7 1 135
#> [186,] 61 4 1 0 1 115
#> [187,] 56 8 1 8 0 120
#> [188,] 61 13 1 12 1 130
#> [189,] 59 11 1 8 1 190
#> [190,] 57 1 0 1 0 126
#> [191,] 57 15 1 13 1 110
#> [192,] 58 5 1 1 1 135
#> [193,] 59 10 0 180 0 160
#> [194,] 61 13 0 13 0 210
#> [195,] 57 3 1 0 0 100
#> [196,] 61 28 1 7 0 133
#> [197,] 61 7 0 7 1 150
#> [198,] 59 13 1 2 0 198
#> [199,] 62 4 1 0 0 160
#> [200,] 60 17 1 8 1 140
#> [201,] 58 3 1 0 1 146
#> [202,] 62 4 1 3 0 173
#> [203,] 58 2 0 30 0 202
#> [204,] 59 1 0 180 0 155
#> [205,] 59 16 1 9 1 133
#> [206,] 63 6 0 28 1 120
#> [207,] 61 13 0 13 0 120
#> [208,] 61 5 0 5 1 110
#> [209,] 61 5 0 5 1 160
#> [210,] 62 1 1 0 1 172
#> [211,] 63 3 1 1 0 180
#> [212,] 61 7 0 180 0 135
#> [213,] 63 4 0 180 1 190
#> [214,] 63 15 1 10 1 126
#> [215,] 64 4 0 180 0 130
#> [216,] 63 4 1 1 0 155
#> [217,] 60 18 1 13 0 132
#> [218,] 61 9 1 9 1 150
#> [219,] 58 9 1 9 0 110
#> [220,] 62 7 0 7 0 150
#> [221,] 59 1 0 22 1 162
#> [222,] 58 2 0 180 0 127
#> [223,] 59 5 1 1 0 148
#> [224,] 60 7 1 1 1 90
#> [225,] 65 13 0 180 1 100
#> [226,] 63 1 0 1 0 162
#> [227,] 63 1 0 1 0 130
#> [228,] 62 6 0 180 0 170
#> [229,] 61 15 1 13 0 170
#> [230,] 59 4 0 4 0 149
#> [231,] 60 3 0 3 0 168
#> [232,] 64 10 1 9 0 160
#> [233,] 62 6 0 6 0 120
#> [234,] 63 12 1 10 0 200
#> [235,] 59 10 0 180 1 130
#> [236,] 61 6 1 1 1 117
#> [237,] 64 12 1 11 0 160
#> [238,] 66 1 1 0 1 120
#> [239,] 64 6 1 0 1 140
#> [240,] 63 4 1 3 0 162
#> [241,] 66 3 1 1 0 127
#> [242,] 61 10 1 2 1 194
#> [243,] 63 12 1 9 0 114
#> [244,] 65 8 1 0 0 168
#> [245,] 65 10 1 8 1 120
#> [246,] 64 21 1 10 0 190
#> [247,] 64 9 0 180 0 150
#> [248,] 61 4 0 180 1 113
#> [249,] 63 16 1 7 1 110
#> [250,] 64 7 0 180 1 120
#> [251,] 66 6 1 1 1 130
#> [252,] 63 12 0 12 1 150
#> [253,] 62 3 1 1 1 199
#> [254,] 65 3 1 0 1 80
#> [255,] 63 2 1 1 0 180
#> [256,] 67 11 0 11 1 100
#> [257,] 64 2 0 2 0 201
#> [258,] 66 18 1 5 0 142
#> [259,] 66 16 1 11 1 169
#> [260,] 62 9 0 180 0 145
#> [261,] 61 14 1 5 0 140
#> [262,] 63 9 1 8 1 160
#> [263,] 63 3 1 2 0 120
#> [264,] 65 8 1 0 1 140
#> [265,] 67 6 0 180 1 170
#> [266,] 65 15 1 11 1 160
#> [267,] 68 5 1 4 1 150
#> [268,] 64 13 1 12 1 150
#> [269,] 64 6 1 0 1 125
#> [270,] 66 7 1 0 1 115
#> [271,] 64 14 1 13 1 150
#> [272,] 65 3 0 3 0 105
#> [273,] 66 3 1 0 1 135
#> [274,] 66 6 1 0 1 140
#> [275,] 65 2 1 1 1 170
#> [276,] 64 10 1 9 1 110
#> [277,] 67 8 1 1 1 130
#> [278,] 68 5 0 5 1 90
#> [279,] 63 10 0 16 1 160
#> [280,] 64 1 0 1 1 120
#> [281,] 68 18 0 180 1 260
#> [282,] 65 18 1 3 0 120
#> [283,] 67 11 0 11 0 150
#> [284,] 68 14 0 79 0 172
#> [285,] 66 12 1 10 1 150
#> [286,] 65 4 1 2 1 145
#> [287,] 63 2 0 180 0 150
#> [288,] 65 11 1 6 0 130
#> [289,] 69 21 1 10 0 180
#> [290,] 69 6 0 180 1 100
#> [291,] 66 9 1 8 0 130
#> [292,] 63 8 0 180 1 120
#> [293,] 65 8 1 0 1 90
#> [294,] 66 3 0 3 1 138
#> [295,] 69 1 1 0 0 170
#> [296,] 67 1 0 180 1 160
#> [297,] 68 10 1 10 1 150
#> [298,] 63 2 1 0 0 99
#> [299,] 67 2 0 180 0 184
#> [300,] 65 6 0 6 0 80
#> [301,] 66 19 1 12 1 150
#> [302,] 67 12 1 12 0 160
#> [303,] 65 4 1 1 0 130
#> [304,] 64 4 0 179 0 160
#> [305,] 66 4 0 180 1 130
#> [306,] 64 11 0 11 0 125
#> [307,] 64 4 0 180 1 140
#> [308,] 67 2 0 18 0 131
#> [309,] 66 7 1 5 1 131
#> [310,] 66 4 0 180 0 177
#> [311,] 68 4 1 0 1 160
#> [312,] 69 4 1 3 1 150
#> [313,] 65 13 1 12 1 130
#> [314,] 69 17 1 10 0 140
#> [315,] 69 8 0 93 0 140
#> [316,] 64 21 0 21 1 155
#> [317,] 65 1 0 1 1 120
#> [318,] 68 18 1 0 1 160
#> [319,] 68 4 0 4 1 190
#> [320,] 71 3 0 5 0 112
#> [321,] 70 7 1 0 1 190
#> [322,] 68 7 0 150 0 210
#> [323,] 70 14 0 171 0 166
#> [324,] 67 10 1 9 0 200
#> [325,] 68 18 1 14 1 170
#> [326,] 69 0 0 0 1 148
#> [327,] 65 2 0 180 0 130
#> [328,] 69 3 1 2 0 151
#> [329,] 67 14 1 13 0 130
#> [330,] 65 14 1 13 1 150
#> [331,] 69 8 0 180 1 180
#> [332,] 71 7 0 7 0 230
#> [333,] 66 2 0 2 1 228
#> [334,] 69 5 0 5 1 142
#> [335,] 71 3 0 103 0 133
#> [336,] 67 1 0 36 1 104
#> [337,] 67 5 0 5 0 130
#> [338,] 68 6 0 180 0 145
#> [339,] 69 8 1 5 1 195
#> [340,] 69 6 1 4 1 174
#> [341,] 72 3 1 0 1 132
#> [342,] 72 7 0 7 1 110
#> [343,] 69 8 1 7 1 108
#> [344,] 66 2 1 1 0 123
#> [345,] 68 18 0 18 1 100
#> [346,] 69 11 1 0 1 120
#> [347,] 69 4 1 3 0 132
#> [348,] 68 2 0 7 1 130
#> [349,] 69 8 1 2 0 121
#> [350,] 67 13 1 9 0 130
#> [351,] 70 3 0 123 0 130
#> [352,] 70 9 0 180 1 142
#> [353,] 72 5 1 4 0 170
#> [354,] 67 22 1 1 1 140
#> [355,] 68 3 0 19 0 135
#> [356,] 67 12 1 8 0 120
#> [357,] 69 1 0 1 1 110
#> [358,] 69 5 0 76 0 120
#> [359,] 72 13 1 11 1 195
#> [360,] 68 10 1 8 1 160
#> [361,] 70 35 1 0 1 105
#> [362,] 72 30 1 0 1 145
#> [363,] 70 7 0 7 0 102
#> [364,] 68 7 1 2 0 135
#> [365,] 73 20 1 0 1 170
#> [366,] 71 6 0 9 0 120
#> [367,] 69 10 1 6 1 120
#> [368,] 70 11 0 180 1 210
#> [369,] 72 19 1 8 0 120
#> [370,] 72 12 1 10 0 170
#> [371,] 67 5 1 0 1 147
#> [372,] 73 13 0 152 1 130
#> [373,] 70 5 0 180 0 150
#> [374,] 72 2 0 2 1 100
#> [375,] 72 6 1 5 0 115
#> [376,] 71 1 0 173 1 188
#> [377,] 68 23 0 180 1 220
#> [378,] 70 3 0 180 0 121
#> [379,] 71 3 1 2 0 150
#> [380,] 68 4 1 3 0 210
#> [381,] 72 5 0 28 0 120
#> [382,] 71 5 0 180 0 191
#> [383,] 73 6 0 180 1 117
#> [384,] 69 16 1 10 1 140
#> [385,] 68 7 0 180 1 130
#> [386,] 72 16 1 1 1 130
#> [387,] 69 1 1 0 0 155
#> [388,] 72 8 1 1 1 150
#> [389,] 73 7 0 7 1 140
#> [390,] 70 3 0 3 1 159
#> [391,] 70 13 1 9 0 100
#> [392,] 72 6 0 180 1 130
#> [393,] 73 0 0 180 1 161
#> [394,] 74 8 1 0 1 85
#> [395,] 71 3 1 1 0 150
#> [396,] 71 15 1 11 0 165
#> [397,] 74 0 1 0 1 90
#> [398,] 70 5 1 0 1 190
#> [399,] 71 3 1 2 1 190
#> [400,] 69 12 1 1 1 149
#> [401,] 74 4 0 4 0 120
#> [402,] 72 5 1 3 1 160
#> [403,] 70 3 0 180 1 154
#> [404,] 73 6 0 180 0 110
#> [405,] 72 15 1 0 1 150
#> [406,] 71 7 1 2 0 143
#> [407,] 74 3 0 3 1 150
#> [408,] 73 17 1 11 0 140
#> [409,] 71 13 1 8 0 121
#> [410,] 69 2 1 1 1 80
#> [411,] 70 4 1 0 1 140
#> [412,] 71 14 1 13 1 170
#> [413,] 72 10 1 8 1 153
#> [414,] 69 7 0 180 1 144
#> [415,] 71 10 1 9 1 120
#> [416,] 75 1 0 1 0 133
#> [417,] 75 2 1 1 0 145
#> [418,] 73 10 1 9 1 146
#> [419,] 73 10 1 10 1 120
#> [420,] 71 2 0 10 1 112
#> [421,] 75 9 1 7 0 140
#> [422,] 75 13 1 1 1 130
#> [423,] 71 11 1 8 0 110
#> [424,] 72 15 1 12 1 120
#> [425,] 73 10 1 8 0 120
#> [426,] 70 7 1 4 0 184
#> [427,] 72 1 1 1 0 168
#> [428,] 70 3 0 3 0 150
#> [429,] 73 12 1 12 1 140
#> [430,] 72 2 0 180 0 120
#> [431,] 75 1 0 180 1 140
#> [432,] 73 4 0 180 0 124
#> [433,] 76 3 1 0 1 120
#> [434,] 71 32 1 12 1 107
#> [435,] 76 5 0 5 1 130
#> [436,] 77 11 0 11 1 150
#> [437,] 75 3 1 1 0 180
#> [438,] 72 7 1 2 0 142
#> [439,] 73 15 0 15 1 160
#> [440,] 73 10 1 10 0 124
#> [441,] 74 3 0 3 1 128
#> [442,] 76 1 0 180 0 114
#> [443,] 74 2 1 1 0 140
#> [444,] 76 8 1 0 1 141
#> [445,] 72 4 0 85 1 120
#> [446,] 72 4 1 3 0 160
#> [447,] 76 17 1 0 1 200
#> [448,] 73 4 1 3 1 125
#> [449,] 76 13 1 10 0 110
#> [450,] 75 4 1 0 1 122
#> [451,] 75 7 0 7 0 190
#> [452,] 75 0 0 0 1 130
#> [453,] 73 13 1 11 0 195
#> [454,] 75 12 0 12 1 160
#> [455,] 74 8 1 0 1 105
#> [456,] 76 13 1 8 1 148
#> [457,] 75 4 1 2 1 188
#> [458,] 76 4 0 4 1 155
#> [459,] 75 1 0 1 1 125
#> [460,] 72 5 0 180 0 120
#> [461,] 76 44 1 10 0 105
#> [462,] 76 5 0 180 0 185
#> [463,] 74 10 1 0 1 135
#> [464,] 76 5 1 0 1 167
#> [465,] 74 8 1 8 1 170
#> [466,] 75 9 0 180 1 140
#> [467,] 77 5 1 0 0 123
#> [468,] 77 12 1 9 1 100
#> [469,] 73 10 1 9 0 146
#> [470,] 77 12 0 180 0 130
#> [471,] 77 1 1 0 1 90
#> [472,] 73 7 1 0 0 174
#> [473,] 75 3 1 1 1 171
#> [474,] 74 9 1 8 0 200
#> [475,] 79 10 1 8 0 190
#> [476,] 78 18 0 18 1 144
#> [477,] 74 2 0 180 0 100
#> [478,] 74 15 0 180 1 172
#> [479,] 74 7 0 7 0 161
#> [480,] 76 13 1 1 1 170
#> [481,] 78 32 1 9 1 198
#> [482,] 79 6 0 180 0 170
#> [483,] 78 0 0 180 1 212
#> [484,] 78 13 1 5 0 130
#> [485,] 75 5 0 119 1 150
#> [486,] 78 15 0 180 1 270
#> [487,] 80 8 0 8 1 120
#> [488,] 75 13 1 6 0 150
#> [489,] 74 10 1 8 0 135
#> [490,] 76 1 0 1 1 83
#> [491,] 79 4 0 80 0 145
#> [492,] 78 12 1 9 0 150
#> [493,] 78 2 1 1 0 130
#> [494,] 75 4 1 0 0 212
#> [495,] 77 2 1 0 1 143
#> [496,] 76 11 1 0 0 120
#> [497,] 77 24 0 24 1 160
#> [498,] 79 8 0 32 1 120
#> [499,] 80 9 0 23 1 128
#> [500,] 78 6 1 0 1 240
#> [501,] 78 11 1 1 1 140
#> [502,] 79 2 1 0 1 121
#> [503,] 78 14 1 0 1 140
#> [504,] 78 11 1 8 1 118
#> [505,] 76 4 0 4 1 160
#> [506,] 79 4 0 4 1 125
#> [507,] 76 10 1 8 0 180
#> [508,] 76 12 1 10 1 127
#> [509,] 78 11 0 180 1 135
#> [510,] 76 1 0 1 1 140
#> [511,] 77 31 1 3 1 161
#> [512,] 76 1 0 1 1 90
#> [513,] 77 7 0 180 1 170
#> [514,] 79 4 1 0 1 120
#> [515,] 81 1 0 180 0 120
#> [516,] 77 9 1 4 0 141
#> [517,] 82 5 0 8 1 120
#> [518,] 80 40 1 0 1 138
#> [519,] 78 4 0 59 1 112
#> [520,] 80 17 1 12 0 100
#> [521,] 76 7 0 161 0 151
#> [522,] 80 15 1 0 1 90
#> [523,] 81 4 1 2 1 126
#> [524,] 80 9 0 118 1 186
#> [525,] 80 6 0 173 1 160
#> [526,] 78 32 0 180 1 130
#> [527,] 79 1 0 37 1 140
#> [528,] 81 3 0 180 0 184
#> [529,] 78 7 0 7 1 147
#> [530,] 77 13 1 0 1 190
#> [531,] 78 4 0 180 0 175
#> [532,] 79 3 0 3 1 101
#> [533,] 76 1 0 166 0 131
#> [534,] 81 4 1 1 1 104
#> [535,] 78 20 1 0 1 109
#> [536,] 78 3 1 1 1 152
#> [537,] 80 2 1 1 0 168
#> [538,] 80 6 1 0 1 119
#> [539,] 78 2 0 180 0 148
#> [540,] 79 10 0 180 1 150
#> [541,] 78 12 0 180 0 134
#> [542,] 79 1 0 125 0 193
#> [543,] 82 21 1 2 0 155
#> [544,] 84 22 1 10 0 180
#> [545,] 79 4 0 4 1 121
#> [546,] 80 6 0 6 1 110
#> [547,] 83 9 1 5 1 170
#> [548,] 82 5 0 180 0 110
#> [549,] 79 7 1 6 0 130
#> [550,] 83 4 0 103 0 97
#> [551,] 81 5 0 177 0 41
#> [552,] 80 11 1 8 0 170
#> [553,] 78 23 1 10 1 145
#> [554,] 78 9 1 4 1 120
#> [555,] 79 1 0 180 1 170
#> [556,] 81 15 0 180 1 140
#> [557,] 80 7 1 0 1 146
#> [558,] 84 5 1 1 1 85
#> [559,] 81 20 1 9 0 170
#> [560,] 81 16 0 16 1 110
#> [561,] 80 11 1 8 0 110
#> [562,] 81 8 0 180 0 146
#> [563,] 80 8 1 7 0 160
#> [564,] 79 7 0 177 0 197
#> [565,] 79 0 1 0 1 96
#> [566,] 85 4 0 180 0 90
#> [567,] 81 2 1 1 0 198
#> [568,] 83 2 0 2 1 155
#> [569,] 80 3 1 1 1 230
#> [570,] 82 23 1 0 0 110
#> [571,] 84 4 0 4 1 85
#> [572,] 84 1 0 38 1 205
#> [573,] 84 4 0 89 1 129
#> [574,] 80 2 1 0 1 130
#> [575,] 80 6 0 71 1 189
#> [576,] 83 1 0 1 1 100
#> [577,] 80 30 1 13 0 220
#> [578,] 83 9 0 180 0 198
#> [579,] 79 14 1 0 0 110
#> [580,] 83 3 0 114 0 98
#> [581,] 83 2 0 154 0 130
#> [582,] 82 0 0 2 1 100
#> [583,] 85 9 1 6 1 160
#> [584,] 83 1 0 180 0 160
#> [585,] 81 4 0 4 0 175
#> [586,] 84 15 1 13 1 110
#> [587,] 81 1 0 1 1 145
#> [588,] 81 12 0 12 1 163
#> [589,] 82 16 1 8 0 103
#> [590,] 86 12 0 180 1 120
#> [591,] 81 19 1 14 0 120
#> [592,] 82 3 1 2 0 130
#> [593,] 82 15 1 0 0 183
#> [594,] 80 2 0 88 0 135
#> [595,] 83 7 0 126 0 135
#> [596,] 86 8 0 8 1 132
#> [597,] 81 16 1 9 0 180
#> [598,] 84 6 0 165 0 145
#> [599,] 86 3 0 3 1 140
#> [600,] 84 3 0 180 1 120
#> [601,] 81 13 0 180 0 152
#> [602,] 85 3 0 3 1 118
#> [603,] 81 2 1 0 1 118
#> [604,] 81 4 0 180 0 160
#> [605,] 83 9 0 180 1 149
#> [606,] 82 1 0 180 1 193
#> [607,] 83 4 0 4 0 130
#> [608,] 87 2 0 5 1 137
#> [609,] 86 12 1 0 1 132
#> [610,] 86 6 1 0 1 140
#> [611,] 84 3 1 2 0 125
#> [612,] 83 10 1 0 1 190
#> [613,] 88 14 1 3 1 130
#> [614,] 84 3 0 3 1 121
#> [615,] 83 13 1 12 0 170
#> [616,] 84 7 1 2 0 148
#> [617,] 84 9 0 92 1 110
#> [618,] 86 4 0 38 1 122
#> [619,] 82 4 0 4 0 130
#> [620,] 86 13 0 177 0 163
#> [621,] 85 3 0 3 1 113
#> [622,] 86 6 1 1 0 112
#> [623,] 88 4 0 4 0 100
#> [624,] 83 20 1 3 1 150
#> [625,] 88 4 0 4 1 115
#> [626,] 85 22 0 22 1 184
#> [627,] 86 9 1 7 1 142
#> [628,] 87 2 0 180 1 130
#> [629,] 88 2 0 180 1 68
#> [630,] 83 3 0 3 1 130
#> [631,] 87 1 0 1 0 170
#> [632,] 85 8 0 8 1 136
#> [633,] 87 29 0 29 1 97
#> [634,] 87 15 1 9 1 138
#> [635,] 89 10 0 46 1 170
#> [636,] 90 14 0 14 1 100
#> [637,] 88 1 0 1 0 135
#> [638,] 86 4 0 180 1 145
#> [639,] 91 8 0 8 0 100
#> [640,] 91 10 0 145 0 135
#> [641,] 86 3 1 0 1 80
#> [642,] 88 8 0 50 1 154
#> [643,] 87 6 0 126 1 168
#> [644,] 86 10 0 180 1 137
#> [645,] 86 9 1 7 0 130
#> [646,] 90 4 1 0 0 121
#> [647,] 91 1 0 1 1 74
#> [648,] 87 43 0 178 1 130
#> [649,] 88 3 1 2 0 159
#> [650,] 89 3 1 1 1 160
#> [651,] 87 7 0 74 1 105
#> [652,] 91 5 0 169 1 176
#> [653,] 89 52 0 52 1 130
#> [654,] 89 4 0 4 1 159
#> [655,] 91 0 0 0 0 0
#> [656,] 89 14 0 180 1 84
#> [657,] 90 18 0 180 0 188
#> [658,] 91 4 1 0 1 120
#> [659,] 94 6 0 50 0 78
#> [660,] 90 1 0 1 1 118
#> [661,] 94 8 0 8 1 142
#> [662,] 92 4 0 76 1 149
#> [663,] 91 1 0 180 0 158
#> [664,] 90 16 0 16 1 106
#> [665,] 96 3 0 12 1 97
#> [666,] 95 8 1 5 1 150
#> [667,] 91 7 0 7 0 135
#> [668,] 92 2 0 2 0 112
#> [669,] 96 3 1 0 1 104
#> [670,] 96 15 1 0 1 140
#>
#> $y
#> [1] 5.0+ 2.0+ 5.0+ 180.0+ 5.0+ 180.0+ 2.0+ 180.0+ 115.0 180.0+
#> [11] 180.0+ 12.0 5.0+ 180.0+ 180.0+ 180.0+ 2.0+ 5.0+ 2.0+ 180.0+
#> [21] 180.0+ 3.0 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [31] 180.0+ 180.0+ 5.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [41] 150.0 180.0+ 180.0+ 180.0+ 180.0+ 6.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [51] 73.0 5.0+ 180.0+ 180.0+ 180.0+ 5.0+ 180.0+ 177.0+ 180.0+ 180.0+
#> [61] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 10.0+ 172.0+ 180.0+ 180.0+ 180.0+
#> [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+ 7.0 88.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 77.0
#> [91] 180.0+ 5.0 180.0+ 180.0+ 180.0+ 180.0+ 4.0+ 85.0 180.0+ 99.0
#> [101] 16.0+ 180.0+ 152.0+ 7.0+ 6.0+ 180.0+ 180.0+ 171.0+ 180.0+ 180.0+
#> [111] 28.0 6.0+ 1.0 9.0+ 180.0+ 180.0+ 180.0+ 180.0+ 7.0+ 180.0+
#> [121] 180.0+ 180.0+ 180.0+ 16.0 180.0+ 180.0+ 12.0+ 134.0+ 2.0 180.0+
#> [131] 180.0+ 180.0+ 180.0+ 180.0+ 165.0 180.0+ 180.0+ 180.0+ 8.0+ 180.0+
#> [141] 180.0+ 0.5 180.0+ 180.0+ 180.0+ 180.0+ 171.0+ 15.0 166.0+ 5.0+
#> [151] 4.0+ 180.0+ 180.0+ 180.0+ 180.0+ 4.0+ 9.0+ 1.0 180.0+ 180.0+
#> [161] 2.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [171] 180.0+ 161.0+ 180.0+ 3.0 1.0 180.0+ 10.0+ 45.0 3.0+ 19.0
#> [181] 180.0+ 9.0+ 172.0+ 172.0+ 24.0 180.0+ 8.0 180.0+ 180.0+ 1.0+
#> [191] 15.0 180.0+ 180.0+ 13.0+ 180.0+ 94.0 7.0 180.0+ 180.0+ 180.0+
#> [201] 3.0+ 180.0+ 30.0 180.0+ 180.0+ 28.0 13.0+ 5.0 5.0+ 1.0
#> [211] 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 4.0+ 180.0+ 180.0+ 9.0 7.0+
#> [221] 22.0 180.0+ 180.0+ 180.0+ 180.0+ 1.0 1.0 180.0+ 180.0+ 4.0+
#> [231] 3.0+ 167.0 6.0+ 180.0+ 180.0+ 180.0+ 12.0 180.0+ 180.0+ 180.0+
#> [241] 3.0+ 88.0 12.0 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [251] 180.0+ 12.0 180.0+ 3.0 180.0+ 11.0+ 2.0+ 18.0+ 180.0+ 180.0+
#> [261] 180.0+ 180.0+ 3.0+ 15.0 180.0+ 180.0+ 5.0+ 13.0 180.0+ 179.0+
#> [271] 14.0+ 3.0 3.0+ 180.0+ 175.0+ 180.0+ 8.0 5.0 16.0 1.0
#> [281] 180.0+ 123.0+ 11.0+ 79.0 80.0 4.0+ 180.0+ 180.0+ 174.0+ 180.0+
#> [291] 180.0+ 180.0+ 8.0+ 3.0 175.0 180.0+ 10.0 180.0+ 180.0+ 6.0
#> [301] 19.0+ 12.0 180.0+ 179.0+ 180.0+ 11.0+ 180.0+ 18.0 7.0+ 180.0+
#> [311] 180.0+ 152.0+ 180.0+ 180.0+ 93.0 21.0+ 1.0 18.0+ 4.0 5.0
#> [321] 7.0+ 150.0 171.0 174.0+ 180.0+ 0.5 180.0+ 180.0+ 180.0+ 14.0+
#> [331] 180.0+ 7.0+ 2.0 5.0+ 103.0 36.0 5.0+ 180.0+ 180.0+ 97.0
#> [341] 180.0+ 7.0 8.0+ 2.0+ 18.0 180.0+ 180.0+ 7.0 8.0+ 13.0+
#> [351] 123.0 180.0+ 180.0+ 51.0 19.0 180.0+ 1.0 76.0 132.0 10.0+
#> [361] 180.0+ 162.0 7.0+ 7.0+ 124.0 9.0 180.0+ 180.0+ 180.0+ 12.0
#> [371] 180.0+ 152.0 180.0+ 2.0 180.0+ 173.0+ 180.0+ 180.0+ 180.0+ 180.0+
#> [381] 28.0 180.0+ 180.0+ 16.0+ 180.0+ 16.0+ 180.0+ 180.0+ 7.0+ 3.0+
#> [391] 13.0+ 180.0+ 180.0+ 180.0+ 3.0+ 180.0+ 0.5 180.0+ 3.0 12.0
#> [401] 4.0+ 180.0+ 180.0+ 180.0+ 180.0+ 180.0+ 3.0 180.0+ 175.0 2.0
#> [411] 180.0+ 14.0+ 10.0+ 180.0+ 179.0+ 1.0 180.0+ 180.0+ 15.0 10.0
#> [421] 180.0+ 13.0 180.0+ 180.0+ 10.0 104.0+ 1.0 3.0+ 12.0 180.0+
#> [431] 180.0+ 180.0+ 180.0+ 177.0+ 5.0 11.0+ 180.0+ 7.0 15.0+ 10.0
#> [441] 3.0 180.0+ 180.0+ 180.0+ 85.0 180.0+ 17.0+ 180.0+ 174.0+ 4.0
#> [451] 7.0 0.5 180.0+ 12.0 180.0+ 180.0+ 46.0 4.0 1.0 180.0+
#> [461] 180.0+ 180.0+ 180.0+ 180.0+ 8.0 180.0+ 5.0 180.0+ 180.0+ 180.0+
#> [471] 1.0 7.0+ 3.0 168.0+ 180.0+ 18.0 180.0+ 180.0+ 7.0 180.0+
#> [481] 32.0 180.0+ 180.0+ 172.0 119.0 180.0+ 8.0 180.0+ 180.0+ 1.0
#> [491] 80.0 180.0+ 180.0+ 4.0+ 2.0 11.0 24.0 32.0 23.0 180.0+
#> [501] 180.0+ 180.0+ 180.0+ 11.0 4.0 4.0 10.0+ 180.0+ 180.0+ 1.0
#> [511] 171.0 1.0 180.0+ 138.0 180.0+ 71.0 8.0 40.0 59.0 17.0
#> [521] 161.0 180.0+ 93.0 118.0 173.0 180.0+ 37.0 180.0+ 7.0+ 22.0
#> [531] 180.0+ 3.0 166.0+ 71.0 20.0+ 3.0+ 10.0 6.0 180.0+ 180.0+
#> [541] 180.0+ 125.0 180.0+ 180.0+ 4.0 6.0 9.0+ 180.0+ 180.0+ 103.0
#> [551] 177.0+ 169.0 70.0 180.0+ 180.0+ 180.0+ 7.0+ 180.0+ 20.0 16.0
#> [561] 180.0+ 180.0+ 180.0+ 177.0+ 0.5 180.0+ 180.0+ 2.0 3.0+ 62.0
#> [571] 4.0 38.0 89.0 180.0+ 71.0 1.0 30.0 180.0+ 180.0+ 114.0
#> [581] 154.0 2.0 180.0+ 180.0+ 4.0+ 180.0+ 1.0 12.0 16.0+ 180.0+
#> [591] 180.0+ 3.0 83.0 88.0 126.0 8.0 180.0+ 165.0 3.0 180.0+
#> [601] 180.0+ 3.0+ 180.0+ 180.0+ 180.0+ 180.0+ 4.0+ 5.0 180.0+ 6.0
#> [611] 180.0+ 180.0+ 14.0 3.0 13.0 180.0+ 92.0 38.0 4.0 177.0
#> [621] 3.0+ 6.0+ 4.0+ 20.0 4.0 22.0 11.0 180.0+ 180.0+ 3.0+
#> [631] 1.0+ 8.0 29.0 180.0+ 46.0 14.0 1.0+ 180.0+ 8.0 145.0
#> [641] 3.0 50.0 126.0 180.0+ 180.0+ 4.0 1.0 178.0+ 75.0 3.0+
#> [651] 74.0 169.0 52.0 4.0 0.5 180.0+ 180.0+ 4.0 50.0 1.0+
#> [661] 8.0+ 76.0 180.0+ 16.0 12.0 8.0 7.0+ 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
#> 0.8556432