
# 6 Super (Machine) Learning

Rachael Phillips

Based on the sl3 R package by Jeremy Coyle, Nima Hejazi, Ivana Malenica, Rachael Phillips, and Oleg Sofrygin.

Updated: 2022-05-03

## Learning Objectives

By the end of this chapter you will be able to:

1. Select an objective function that (i) aligns with the intention of the analysis and (ii) is optimized by the target parameter.

2. Assemble a diverse library of learners to be considered in the Super Learner ensemble. In particular, you should be able to:

1. Customize a learner by modifying it’s tuning parameters.
2. Create several different versions of the same learner at once by specifying a grid of tuning parameters.
3. Curate covariate screening pipelines in order to pass a screener’s output, a subset of covariates, as input for another learner that will use the subset of covariates selected by the screener to model the data.
3. Specify the learner for ensembling (the metalearner) such that it corresponds to your objective function.

4. Fit the Super Learner ensemble with nested cross-validation to obtain an estimate of the performance of the ensemble itself on out-of-sample data.

5. Obtain sl3 variable importance metrics.

6. Interpret the fit for discrete and continuous Super Learners’ from the cross-validated risk table and the coefficients.

7. Justify the base library of machine learning algorithms and the ensembling learner in terms of the prediction problem, statistical model $$\M$$, data sparsity, and the dimensionality of the covariates.

## Motivation

• A common task in data analysis is prediction – using the observed data (input variables and outcomes) to learn a function that can map new input variables into a predicted outcome.
• For some data, algorithms that learn complex relationships between variables are necessary to adequately model the data. For other data, main terms regression models might fit the data quite well.
• It is generally impossible to know a priori which algorithm will be the best for a given data set and prediction problem. It’s like picking the winner of The Great British Bake Off at the start of the first week!
• The Super Learner solves this issue of algorithm selection by creating an ensemble of many algorithms, from the simplest (intercept-only) to most complex (neural nets, tree-based methods, support vector machines, etc.).
• Super Learner works by using cross-validation in a manner that theoretically (in large samples) guarantees the resulting fit will be as good as possible, given the algorithms provided.

## 6.1 Introduction

In Chapter 1, we introduced the Roadmap for Targeted Learning as a general template to translate real-world data applications into formal statistical estimation problems. The first steps of this roadmap define the statistical estimation problem, which establish

1. The data $O$ as a random variable, or equivalently, a realization of a particular experiment/study, which has probability distribution $$P_0$$. This is written $$O \sim P_0$$, and $$P_0$$ is also commonly referred to as the data-generating process (DGP) and also the data-generating distribution (DGD). The data structure $$O$$ is comprised of variables, such as a vector of covariates $$W$$, a treatment or exposure $$A$$, and an outcome $$Y$$, $$O=(W,A,Y) \sim P_0$$. We often observe the random variable $$O$$ $$n$$ times, by repeating the common experiment $$n$$ times. For example, $$O_1,\ldots, O_n$$ random variables could be the result of a random sample of $$n$$ subjects from a population, collecting baseline characteristics $$W$$, randomly assigning treatment $$A$$, and then later measuring an outcome $$Y$$.
2. A statistical model $$\M$$ as a set of possible probability distributions that could have given rise to the data. It’s essential for $$\M$$ to only constrained by factual subject-matter knowledge in order to guarantee $$P_0$$ resides in the statistical model, written $$P_0 \in \M$$. Continuing the example from step 1, the following restrictions could be placed on the statistical model: the $$O_1, \ldots, O_n$$ observations in the data are independent and identically distributed (i.i.d.), the assignment of treatment $$A$$ was random and not based on covariates $$W$$.
3. A translation of the scientific question of interest into a function of $$P_0$$, the target statistical estimand $$\Psi(P_0)$$. For example, we might be interested in the average difference in mean outcomes under treatment $$A=1$$ versus placebo $$A=0$$: $$\Psi(P_0)=E_{P_0}\Big[E_{P_0}(Y|A=1,W)−E_{P_0}(Y|A=0,W)\Big]$$. Note that, if the scientific question is causal, then it’s translation will produce a target causal estimand; another layer of translation, identifiability, is required to express the target causal estimand as a function of the observed data distribution $$P_0$$. See causal target parameters for more information on causal quantities, causal models and identifiability.

Once the statistical estimation problem has been established, then the estimator can be constructed. Estimators (also referred to as algorithms and learners) are functions that take as input the observed data, and return as output an estimate of $$P_0$$ or some feature of $$P_0$$ (such as a component of the target estimand). We will use the Super Learner (SL) algorithm to estimate a prediction function, and then we will use the SL estimator of the prediction function to predict outcomes from new input (e.g., covariate/predictor) data. Occasionally, the prediction function itself is the target estimand; more commonly, the prediction function is a component of a target estimand.

Consider an example where we need to estimate the prediction function for $$E_{P_0}(Y|A,W)$$ (the conditional mean outcome, given treatment $$A$$ and baseline covariates $$W$$), so we can predict what the outcomes would have been under a hypothetical scenario where all subjects received treatment $$A=1$$. In order for this estimator to output predictions that correspond to outcomes in a world where all subjects received treatment $$A=1$$, we would need to supply the estimator with input data that reflects it; specifically, the baseline covariate information $$W$$ would remain the same treatment as it is in the observed data, but the treatment $$A$$ would be set to 1 for all individuals, regardless of whether or not they actually received it. This learning paradigm corresponds to estimation of a component of the target statistical estimand mentioned in step 3 above, the $$E_{P_0}(Y|A=1,W)$$ component of $$\Psi(P_0)$$.

There are various strategies that estimators can employ to model relationships from the observed data, and there is no “one fits all” algorithm in the realm of real-world data science. However, the statistical performance of algorithms’ (e.g., mean squared error) can be used to compare them. Therefore, algorithm selection should be driven criteria that have been (i) proven to optimize relevant statistical properties (e.g., provide theoretical guarantees) and (ii) shown to be reliable in practice (e.g., with complex real-world data). The SL is an algorithm that is equipped with such a standard, the cross-validation criterion, which ensures in large samples that the SL will perform atleast as well as the unknown best-performing candidate algorithm (van der Laan and Dudoit 2003; van der Vaart, Dudoit, and van der Laan 2006; van der Laan, Polley, and Hubbard 2007). Also, as an ensemble machine learning algorithm, SL leverages information learned from a variety of candidate algorithms by creating a weighted combination of them (i.e., metalearning). In summary, SL represents a practical approach for principled machine learning. It has been shown to be adaptive and robust, even in small samples (Polley and van der Laan 2010).

### 6.1.1 Candidate Learners and Ensembling

The set of algorithms considering by the SL (also referred to as “library”) should consist of those that align with what’s known about the DGP and what is not known about the DGP. In other words, the learners in the library should be tailored to respect the statistical model $$\mathcal{M}$$, both in terms of

1. the restrictions placed on $$\mathcal{M}$$, so the candidate algorithms represent functions that align with the knowledge about the DGP, and
2. the vastness of $$\mathcal{M}$$, so the library is able to adapt to a diversity of possible forms for the DGP, which can be acheived by including a variety of learning strategies (e.g., that range from parametric regression models to multi-step algorithms involving screening covariates, penalizations, optimizing tuning parameters, etc.)

#### 6.1.1.1 Example: Respecting known bounds on the outcome

Suppose it is known that the outcome cannot take certain values, (e.g., the outcome is always a positive real number). The statistical model should be constrained to reflect these outcome bounds, and in order for the learners in the library to respect the statistical model, the learners should be constructed such that their predictions do not fall outside of the outcome bounds. For learners that allow link functions (e.g., generalized elastic-net regression models), different link functions can be chosen to match known outcome bounds. If a learner does not support link functions, or some other bounding criteria during model fitting, then there is a possibility that the learner will yield predictions that fall outside of known outcome bounds. In this scenario, the predictions that are not within known bounds could be truncated, which might be fine for learners that seldomly produce predictions that violate known outcome bounds.

In general, the use of link function(s) is more sensible, since it formulates a model for the function that respects the statistical model, and then optimizes the fit in that model. The truncation option optimizes in a model that’s too big, losing information, and then corrects ad hoc. However, it might be limiting to only include learners that support some desired link function (e.g., a library of several logistic regression models), since a diversity of possible DGPs might not be captured by this library.

Recall that SL is a weighted combination of this library of candidate algorithms, and not all weighted combinations are created equally. In order to ensure that the SL has the same bounds on the predictions as the candidates in the library, SL’s weighted combination should be a convex combination (i.e., weights are non-negative and sum to one). The most simple example of a convex combination would be the so-called “discrete SL” or “cross-validated selector”, which uses a metalearner that assigns a weight of one to the best candidate algorithm in the library, and a weight of zero to all others (where best is described in step 4(a) in the step-by-step overview below). More flexible metalearners, like the default metalearner in sl3, are those that allow multiple algorithms to have nonzero weights and still enforce a convex combination.

Keep in mind

The learners in the SL library and should be justified in terms of the prediction problem, statistical model $$\M$$, data sparsity (e.g., number of independent samples, number of events for rare binary outcomes), and the number of covariates. The metalearning strategy should be similarly justified.

### 6.1.2 Fitting the Super Learner

#### 6.1.2.1 Cross-validation

• There are many different cross-validation schemes, which are designed to accommodate different study designs, data structures, and prediction problems. See cross-validation for more detail.

The figure above shows an example of $$V$$-fold cross-validation with $$V=10$$ folds, and this is the default cross-validation structure in the sl3 R package. The darker boxes represent the so-called “validation data” and the lighter boxes represent the so-called “training data”. The following details are important to notice:

• Across all folds, there are $$V$$ (10) copies of the dataset. The only difference between each copy is the coloring, which distinguishes the subset of the data that’s considered as the training data from the subset that’s considered as the validation data.
• Within each fold 1/$$V$$ (1/10) of the data is the validation data.
• Across all folds, all of the data will be considered as validation data and no observation will be included twice as validation data. Therefore, the total number of validation data observations across all of the folds is equal to the total number of observations in the data.

#### 6.1.2.2 Step-by-step procedure with V-fold Cross-validation

1. Fit each learner (say there are $$K$$ learners) on the whole dataset. We refer to these learners that are trained on the whole dataset as “full-fit” learners.
2. Break up the data evenly into $$V$$ disjoint subsets. Separately, create $$V$$ copies of the data. For each copy $$v$$, where $$v=1,\ldots,V$$, create the $$V$$ folds by labelling the portion of the data that was included in subset $$v$$ as the validation sample, and the labelling what’s remaining of the data as the training sample.
3. For each fold $$v$$, $$v=1,\ldots,V$$, fit each learner (say there are $$K$$ learners) on the training sample and predict the validation sample outcomes by providing each fitted learner with the validation sample covariates as input. Notice that each learner will be fit $$V$$ times. We refer to these learners that are trained across the $$V$$ cross-validation folds as “cross-validated fit” learners.
4. Combine the validation sample predictions from all folds and all learners to create the so-called $$K$$ column matrix of “cross-validated predictions”. This matrix is also commonly referred to as the $$Z$$ matrix. Notice that it contains, for each learner, out-of-sample predictions for all of the observations in the data.
5. Train the metalearner (e.g., a non-negative least squares regression) on data with predictors and outcomes being the $$Z$$ matrix and the observed data outcomes, respectively. The metalearner — just like any ordinary ML algorithm — estimates the parameters of it’s model using the training data and afterwards, the fitted model can be used to obtain predicted outcomes from new input data. What’s special about the metalearner is that it’s estimated model parameters (e.g., regression coefficients) correspond to it’s predictors, which are the variables in the $$Z$$ matrix, the $$K$$ learners’ predictions. Once the metalearner is fit, it can be used to obtain predicted outcomes from new input data; that is, new $$K$$ learners predictions’ can be supplied to the fitted metalearner in order to obtain predicted outcomes.
6. The fitted metalearner and the full-fit learners define the weighted combination of the $$K$$ learners, finalizing the Super Learner (SL) fit. To obtain SL predictions the full-fit learners’ predictions are first obtained and then fed as input to the fitted metalearner; the metalearner’s output is the SL predictions.

### 6.1.3 Theoretical foundations

This section is under construction.

For more detail on Super Learner algorithm we refer the reader to Polley and van der Laan (2010) and van der Laan, Polley, and Hubbard (2007). The optimality results for the cross-validation selector among a family of algorithms were established in van der Laan and Dudoit (2003) and extended in van der Vaart, Dudoit, and van der Laan (2006).

## sl3 “Microwave Dinner” Implementation

We begin by illustrating the core functionality of the SL algorithm as implemented in sl3.

The sl3 implementation consists of the following steps:

1. Load the necessary libraries and data
2. Define the machine learning task
3. Make an SL by creating library of base learners and a metalearner
4. Train the SL on the machine learning task
5. Obtain predicted values

### WASH Benefits Study Example

Using the WASH Benefits Bangladesh data, we are interested in predicting weight-for-height z-score whz using the available covariate data. More information on this dataset, and all other data that we will work with, are described in this chapter of the tlverse handbook. Let’s begin!

### 0. Load the necessary libraries and data

First, we will load the relevant R packages, set a seed, and load the data.

library(data.table)
library(dplyr)
library(ggplot2)
library(SuperLearner)
library(origami)
library(sl3)
library(knitr)
library(kableExtra)

# load data set and take a peek
paste0(
"https://raw.githubusercontent.com/tlverse/tlverse-data/master/",
"wash-benefits/washb_data.csv"
),
stringsAsFactors = TRUE
)
kable() %>%
scroll_box(width = "100%", height = "300px")
whz tr fracode month aged sex momage momedu momheight hfiacat Nlt18 Ncomp watmin elec floor walls roof asset_wardrobe asset_table asset_chair asset_khat asset_chouki asset_tv asset_refrig asset_bike asset_moto asset_sewmach asset_mobile
0.00 Control N05265 9 268 male 30 Primary (1-5y) 146.40 Food Secure 3 11 0 1 0 1 1 0 1 1 1 0 1 0 0 0 0 1
-1.16 Control N05265 9 286 male 25 Primary (1-5y) 148.75 Moderately Food Insecure 2 4 0 1 0 1 1 0 1 0 1 1 0 0 0 0 0 1
-1.05 Control N08002 9 264 male 25 Primary (1-5y) 152.15 Food Secure 1 10 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 1
-1.26 Control N08002 9 252 female 28 Primary (1-5y) 140.25 Food Secure 3 5 0 1 0 1 1 1 1 1 1 0 0 0 1 0 0 1
-0.59 Control N06531 9 336 female 19 Secondary (>5y) 150.95 Food Secure 2 7 0 1 0 1 1 1 1 1 1 1 0 0 0 0 0 1
-0.51 Control N06531 9 304 male 20 Secondary (>5y) 154.20 Severely Food Insecure 0 3 1 1 0 1 1 0 0 0 0 1 0 0 0 0 0 1

### 1. Define the machine learning task

To define the machine learning task (predict weight-for-height Z-score whz using the available covariate data), we need to create an sl3_Task object.

The sl3_Task keeps track of the roles the variables play in the machine learning problem, the data, and any metadata (e.g., observational-level weights, IDs, offset).

Also, if we had missing outcomes, we would need to set drop_missing_outcome = TRUE when we create the task. In the following chapter, we need to estimate this missingness mechanism; which is the conditional probably that the outcome is observed, given the history (i.e., variables that were measured before the missingness). Estimating the missingness mechanism requires learning a prediction function that outputs the predicted probability that a unit is missing, given their history.

# specify the outcome and covariates
outcome <- "whz"
covars <- colnames(washb_data)[-which(names(washb_data) == outcome)]

data = washb_data,
covariates = covars,
outcome = outcome
)
Warning in process_data(data, nodes, column_names = column_names, flag = flag, :
Missing covariate data detected: imputing covariates.

This warning is important. The task just imputed missing covariates for us. Specifically, for each covariate column with missing values, sl3 uses the median to impute missing continuous covariates, and the mode to impute binary and categorical covariates.

Also, for each covariate column with missing values, sl3 adds an additional column indicating whether or not the value was imputed, which is particularly handy when the missingness in the data might be informative.

Also, notice that we did not specify the number of folds, or the loss function in the task. The default cross-validation scheme is V-fold, with $$V=10$$ number of folds.

Let’s visualize our washb_task:

washb_task
A sl3 Task with 4695 obs and these nodes:
$covariates [1] "tr" "fracode" "month" "aged" [5] "sex" "momage" "momedu" "momheight" [9] "hfiacat" "Nlt18" "Ncomp" "watmin" [13] "elec" "floor" "walls" "roof" [17] "asset_wardrobe" "asset_table" "asset_chair" "asset_khat" [21] "asset_chouki" "asset_tv" "asset_refrig" "asset_bike" [25] "asset_moto" "asset_sewmach" "asset_mobile" "delta_momage" [29] "delta_momheight"$outcome
[1] "whz"

$id NULL$weights
NULL

$offset NULL$time
NULL

We can’t see when we print the task, but the default cross-validation fold structure ($$V$$-fold cross-validation with $$V$$=10 folds) was created when we defined the task.

length(washb_task$folds) # how many folds? [1] 10 head(washb_task$folds[[1]]$training_set) # row indexes for fold 1 training [1] 1 2 3 4 5 6 head(washb_task$folds[[1]]$validation_set) # row indexes for fold 1 validation [1] 12 21 29 41 43 53 any( washb_task$folds[[1]]$training_set %in% washb_task$folds[[1]]$validation_set ) [1] FALSE Tip: If you type washb_task$ and then press the tab button (you will need to press tab twice if you’re not in RStudio), you can view all of the active and public fields and methods that can be accessed from the washb_task object.

### 2. Make a Super Learner

Now that we have defined our machine learning problem with the sl3_Task, we are ready to make the Super Learner (SL). This requires specification of

• A set of candidate machine learning algorithms, also commonly referred to as a library of learners. The set should include a diversity of algorithms that are believed to be consistent with the true data-generating distribution.
• A metalearner, to ensemble the base learners.

We might also incorporate

• Feature selection, to pass only a subset of the predictors to the algorithm.
• Hyperparameter specification, to tune base learners.

Learners have properties that indicate what features they support. We may use sl3_list_properties() to get a list of all properties supported by at least one learner.

sl3_list_properties()
[1] "binomial"      "categorical"   "continuous"    "cv"
[5] "density"       "h2o"           "ids"           "importance"
[9] "offset"        "preprocessing" "sampling"      "screener"
[13] "timeseries"    "weights"       "wrapper"      

Since we have a continuous outcome, we may identify the learners that support this outcome type with sl3_list_learners().

sl3_list_learners("continuous")
[1] "Lrnr_arima"                     "Lrnr_bartMachine"
[3] "Lrnr_bayesglm"                  "Lrnr_bilstm"
[5] "Lrnr_bound"                     "Lrnr_caret"
[7] "Lrnr_cv_selector"               "Lrnr_dbarts"
[9] "Lrnr_earth"                     "Lrnr_expSmooth"
[11] "Lrnr_gam"                       "Lrnr_gbm"
[13] "Lrnr_glm"                       "Lrnr_glm_fast"
[15] "Lrnr_glmnet"                    "Lrnr_grf"
[17] "Lrnr_gru_keras"                 "Lrnr_gts"
[19] "Lrnr_h2o_glm"                   "Lrnr_h2o_grid"
[21] "Lrnr_hal9001"                   "Lrnr_HarmonicReg"
[23] "Lrnr_hts"                       "Lrnr_lightgbm"
[25] "Lrnr_lstm_keras"                "Lrnr_mean"
[27] "Lrnr_multiple_ts"               "Lrnr_nnet"
[29] "Lrnr_nnls"                      "Lrnr_optim"
[31] "Lrnr_pkg_SuperLearner"          "Lrnr_pkg_SuperLearner_method"
[33] "Lrnr_pkg_SuperLearner_screener" "Lrnr_polspline"
[35] "Lrnr_randomForest"              "Lrnr_ranger"
[37] "Lrnr_rpart"                     "Lrnr_rugarch"
[39] "Lrnr_screener_correlation"      "Lrnr_solnp"
[41] "Lrnr_stratified"                "Lrnr_svm"
[43] "Lrnr_tsDyn"                     "Lrnr_xgboost"                  

Now that we have an idea of some learners, we can construct them using the make_learner function or the new method.

# choose base learners
lrn_glm <- make_learner(Lrnr_glm)
lrn_mean <- Lrnr_mean$new() We can customize learner hyperparameters to incorporate a diversity of different settings. Documentation for the learners and their hyperparameters can be found in the sl3 Learners Reference. lrn_lasso <- make_learner(Lrnr_glmnet) # alpha default is 1 lrn_ridge <- Lrnr_glmnet$new(alpha = 0)
lrn_enet.5 <- make_learner(Lrnr_glmnet, alpha = 0.5)

lrn_polspline <- Lrnr_polspline$new() lrn_ranger100 <- make_learner(Lrnr_ranger, num.trees = 100) lrn_hal_faster <- Lrnr_hal9001$new(max_degree = 2, reduce_basis = 0.05)

xgb_fast <- Lrnr_xgboost$new() # default with nrounds = 20 is pretty fast xgb_50 <- Lrnr_xgboost$new(nrounds = 50)

We can use Lrnr_define_interactions to define interaction terms among covariates. The interactions should be supplied as list of character vectors, where each vector specifies an interaction. For example, we specify interactions below between (1) tr (whether or not the subject received the WASH intervention) and elec (whether or not the subject had electricity); and between (2) tr and hfiacat (the subject’s level of food security).

interactions <- list(c("elec", "tr"), c("tr", "hfiacat"))
# main terms as well as the interactions above will be included
lrn_interaction <- make_learner(Lrnr_define_interactions, interactions)

What we just defined above is incomplete. In order to fit learners with these interactions, we need to create a Pipeline. A Pipeline is a set of learners to be fit sequentially, where the fit from one learner is used to define the task for the next learner. We need to create a Pipeline with the interaction defining learner and another learner that incorporate these terms when fitting a model. Let’s create a learner pipeline that will fit a linear model with the combination of main terms and interactions terms, as specified in lrn_interaction.

# we already instantiated a linear model learner, no need to do that again
lrn_glm_interaction <- make_learner(Pipeline, lrn_interaction, lrn_glm)
lrn_glm_interaction
[1] "Lrnr_define_interactions_TRUE"
[1] "Lrnr_glm_TRUE"

We can also include learners from the SuperLearner R package.

lrn_bayesglm <- Lrnr_pkg_SuperLearner$new("SL.bayesglm") Here is a fun trick to create customized learners over a grid of parameters. # I like to crock pot my SLs grid_params <- list( cost = c(0.01, 0.1, 1, 10, 100, 1000), gamma = c(0.001, 0.01, 0.1, 1), kernel = c("polynomial", "radial", "sigmoid"), degree = c(1, 2, 3) ) grid <- expand.grid(grid_params, KEEP.OUT.ATTRS = FALSE) svm_learners <- apply(grid, MARGIN = 1, function(tuning_params) { do.call(Lrnr_svm$new, as.list(tuning_params))
})
grid_params <- list(
max_depth = c(2, 4, 6),
eta = c(0.001, 0.1, 0.3),
nrounds = 100
)
grid <- expand.grid(grid_params, KEEP.OUT.ATTRS = FALSE)
grid
max_depth   eta nrounds
1         2 0.001     100
2         4 0.001     100
3         6 0.001     100
4         2 0.100     100
5         4 0.100     100
6         6 0.100     100
7         2 0.300     100
8         4 0.300     100
9         6 0.300     100

xgb_learners <- apply(grid, MARGIN = 1, function(tuning_params) {
do.call(Lrnr_xgboost$new, as.list(tuning_params)) }) xgb_learners [[1]] [1] "Lrnr_xgboost_100_1_2_0.001" [[2]] [1] "Lrnr_xgboost_100_1_4_0.001" [[3]] [1] "Lrnr_xgboost_100_1_6_0.001" [[4]] [1] "Lrnr_xgboost_100_1_2_0.1" [[5]] [1] "Lrnr_xgboost_100_1_4_0.1" [[6]] [1] "Lrnr_xgboost_100_1_6_0.1" [[7]] [1] "Lrnr_xgboost_100_1_2_0.3" [[8]] [1] "Lrnr_xgboost_100_1_4_0.3" [[9]] [1] "Lrnr_xgboost_100_1_6_0.3" Did you see Lrnr_caret when we called sl3_list_learners(c("binomial"))? All we need to specify to use this popular algorithm as a candidate in our SL is the algorithm we want to tune, which is passed as method to caret::train(). # Unlike xgboost, I have no idea how to tune a neural net or BART machine, so # I let caret take the reins lrnr_caret_nnet <- make_learner(Lrnr_caret, algorithm = "nnet") lrnr_caret_bartMachine <- make_learner(Lrnr_caret, algorithm = "bartMachine", method = "boot", metric = "Accuracy", tuneLength = 10 ) In order to assemble the library of learners, we need to Stack them together. A Stack is a special learner and it has the same interface as all other learners. What makes a stack special is that it combines multiple learners by training them simultaneously, so that their predictions can be either combined or compared. stack <- make_learner( Stack, lrn_glm, lrn_polspline, lrn_enet.5, lrn_ridge, lrn_lasso, xgb_50 ) stack [1] "Lrnr_glm_TRUE" [2] "Lrnr_polspline_5" [3] "Lrnr_glmnet_NULL_deviance_10_0.5_100_TRUE_FALSE" [4] "Lrnr_glmnet_NULL_deviance_10_0_100_TRUE_FALSE" [5] "Lrnr_glmnet_NULL_deviance_10_1_100_TRUE_FALSE" [6] "Lrnr_xgboost_50_1"  We can also stack the learners by first creating a vector, and then instantiating the stack. I prefer this method, since it easily allows us to modify the names of the learners. # named vector of learners first learners <- c( lrn_glm, lrn_polspline, lrn_enet.5, lrn_ridge, lrn_lasso, xgb_50 ) names(learners) <- c( "glm", "polspline", "enet.5", "ridge", "lasso", "xgboost50" ) # next make the stack stack <- make_learner(Stack, learners) # now the names are pretty stack [1] "glm" "polspline" "enet.5" "ridge" "lasso" "xgboost50" We’re jumping ahead a bit, but let’s check something out quickly. It’s straightforward, and just one more step, to set up this stack such that all of the learners will train in a cross-validated manner. cv_stack <- Lrnr_cv$new(stack)
cv_stack
[1] "Lrnr_cv"
[1] "glm"       "polspline" "enet.5"    "ridge"     "lasso"     "xgboost50"

#### Screening Algorithms for Feature Selection

We can optionally select a subset of available covariates and pass only those variables to the modeling algorithm. The current set of learners that can be used for prescreening covariates is included below.

• Lrnr_screener_importance selects num_screen (default = 5) covariates based on the variable importance ranking provided by the learner. Any learner with an importance method can be used in Lrnr_screener_importance; and this currently includes Lrnr_ranger, Lrnr_randomForest, and Lrnr_xgboost.
• Lrnr_screener_coefs, which provides screening of covariates based on the magnitude of their estimated coefficients in a (possibly regularized) GLM. The threshold (default = 1e-3) defines the minimum absolute size of the coefficients, and thus covariates, to be kept. Also, a max_retain argument can be optionally provided to restrict the number of selected covariates to be no more than max_retain.
• Lrnr_screener_correlation provides covariate screening procedures by running a test of correlation (Pearson default), and then selecting the (1) top ranked variables (default), or (2) the variables with a pvalue lower than some pre-specified threshold.
• Lrnr_screener_augment augments a set of screened covariates with additional covariates that should be included by default, even if the screener did not select them. An example of how to use this screener is included below.

Let’s consider screening covariates based on their randomForest variable importance ranking (ordered by mean decrease in accuracy). To select the top 5 most important covariates according to this ranking, we can combine Lrnr_screener_importance with Lrnr_ranger (limiting the number of trees by setting ntree = 20).

Hang on! Before you think it – we will confess: Bob Ross and us both know that 20 trees makes for a lonely forest, and we shouldn’t consider it, but these are the sacrifices we make for this chapter to be built in time!

miniforest <- Lrnr_ranger$new( num.trees = 20, write.forest = FALSE, importance = "impurity_corrected" ) # learner must already be instantiated, we did this when we created miniforest screen_rf <- Lrnr_screener_importance$new(learner = miniforest, num_screen = 5)
screen_rf
[1] "Lrnr_screener_importance_5"

# which covariates are selected on the full data?
screen_rf$train(washb_task) [1] "Lrnr_screener_importance_5"$selected
[1] "aged"      "month"     "tr"        "momheight" "momedu"   

An example of how to format Lrnr_screener_augment is included below for clarity.

keepme <- c("aged", "momage")
# screener must already be instantiated, we did this when we created screen_rf
screen_augment_rf <- Lrnr_screener_augment$new( screener = screen_rf, default_covariates = keepme ) screen_augment_rf [1] "Lrnr_screener_augment_c(\"aged\", \"momage\")" Selecting covariates with non-zero lasso coefficients is quite common. Let’s construct Lrnr_screener_coefs screener that does just that, and test it out. # we already instantiated a lasso learner above, no need to do it again screen_lasso <- Lrnr_screener_coefs$new(learner = lrn_lasso, threshold = 0)
screen_lasso
[1] "Lrnr_screener_coefs_0_NULL_2"

To pipe only the selected covariates to the modeling algorithm, we need to make a Pipeline, similar to the one we built for the regression model with interaction terms.

screen_rf_pipe <- make_learner(Pipeline, screen_rf, stack)
screen_lasso_pipe <- make_learner(Pipeline, screen_lasso, stack)

Now, these learners will be preceded by a screening step.

We also consider the original stack, to compare how the feature selection methods perform in comparison to the methods without feature selection.

Analogous to what we have seen before, we have to stack the pipeline and original stack together, so we may use them as base learners in our super learner.

# pretty names again
learners2 <- c(learners, screen_rf_pipe, screen_lasso_pipe)
names(learners2) <- c(names(learners), "randomforest_screen", "lasso_screen")

fancy_stack <- make_learner(Stack, learners2)
fancy_stack
[1] "glm"                 "polspline"           "enet.5"
[4] "ridge"               "lasso"               "xgboost50"
[7] "randomforest_screen" "lasso_screen"       

We will use the default metalearner, which uses Lrnr_solnp to provide fitting procedures for a pairing of loss function and metalearner function. This default metalearner selects a loss and metalearner pairing based on the outcome type. Note that any learner can be used as a metalearner.

Now that we have made a diverse stack of base learners, we are ready to make the SL. The SL algorithm fits a metalearner on the validation set predictions/losses across all folds.

sl <- make_learner(Lrnr_sl, learners = fancy_stack)

We can also use Lrnr_cv to build a SL, cross-validate a stack of learners to compare performance of the learners in the stack, or cross-validate any single learner (see “Cross-validation” section of this sl3 introductory tutorial).

Furthermore, we can Define New sl3 Learners which can be used in all the places you could otherwise use any other sl3 learners, including Pipelines, Stacks, and the SL.

Recall that the discrete SL, or cross-validated selector, is a metalearner that assigns a weight of 1 to the learner with the lowest cross-validated empirical risk, and weight of 0 to all other learners. This metalearner specification can be invoked with Lrnr_cv_selector.

discrete_sl_metalrn <- Lrnr_cv_selector$new() discrete_sl <- Lrnr_sl$new(
learners = fancy_stack,
metalearner = discrete_sl_metalrn
)

### 3. Train the Super Learner on the machine learning task

The SL algorithm fits a metalearner on the validation-set predictions in a cross-validated manner, thereby avoiding overfitting.

Now we are ready to train our SL on our sl3_task object, washb_task.

set.seed(4197)
sl_fit <- sl$train(washb_task) ### 4. Obtain predicted values Now that we have fit the SL, we are ready to calculate the predicted outcome for each subject. # we did it! now we have SL predictions sl_preds <- sl_fit$predict()
[1] -0.65442 -0.77055 -0.67359 -0.65109 -0.65577 -0.65673

We can also obtain a summary of the results.

sl_fit$cv_risk(loss_fun = loss_squared_error) learner coefficients risk se fold_sd 1: glm 0.055571 1.0202 0.023955 0.067500 2: polspline 0.055556 1.0208 0.023577 0.067921 3: enet.5 0.055564 1.0131 0.023598 0.065732 4: ridge 0.055570 1.0153 0.023739 0.065299 5: lasso 0.055564 1.0130 0.023592 0.065840 6: xgboost50 0.055591 1.1136 0.025262 0.077580 7: randomforest_screen_glm 0.055546 1.0271 0.024119 0.069913 8: randomforest_screen_polspline 0.055561 1.0236 0.024174 0.068710 9: randomforest_screen_enet.5 0.055546 1.0266 0.024101 0.070117 10: randomforest_screen_ridge 0.055546 1.0268 0.024120 0.069784 11: randomforest_screen_lasso 0.055546 1.0266 0.024101 0.070135 12: randomforest_screen_xgboost50 0.055523 1.1399 0.026341 0.100112 13: lasso_screen_glm 0.055559 1.0164 0.023542 0.065018 14: lasso_screen_polspline 0.055559 1.0177 0.023520 0.065566 15: lasso_screen_enet.5 0.055559 1.0163 0.023544 0.065017 16: lasso_screen_ridge 0.055559 1.0166 0.023553 0.064869 17: lasso_screen_lasso 0.055559 1.0163 0.023544 0.065020 18: lasso_screen_xgboost50 0.055521 1.1256 0.025939 0.084270 19: SuperLearner NA 1.0135 0.023615 0.067434 fold_min_risk fold_max_risk 1: 0.89442 1.1200 2: 0.89892 1.1255 3: 0.88839 1.1058 4: 0.88559 1.1063 5: 0.88842 1.1060 6: 0.96019 1.2337 7: 0.90251 1.1326 8: 0.90167 1.1412 9: 0.90030 1.1319 10: 0.90068 1.1311 11: 0.90043 1.1321 12: 0.92377 1.2549 13: 0.90204 1.1156 14: 0.89742 1.1162 15: 0.90184 1.1154 16: 0.90120 1.1146 17: 0.90183 1.1154 18: 0.96251 1.2327 19: 0.88685 1.1102 ## Cross-validated Super Learner We can cross-validate the SL to see how well the SL performs on unseen data, and obtain an estimate of the cross-validated risk of the SL. This estimation procedure requires an outer/external layer of cross-validation, also called nested cross-validation, which involves setting aside a separate holdout sample that we don’t use to fit the SL. This external cross-validation procedure may also incorporate 10 folds, which is the default in sl3. However, we will incorporate 2 outer/external folds of cross-validation for computational efficiency. We also need to specify a loss function to evaluate SL. Documentation for the available loss functions can be found in the sl3 Loss Function Reference. washb_task_new <- make_sl3_Task( data = washb_data, covariates = covars, outcome = outcome, folds = origami::make_folds(washb_data, fold_fun = folds_vfold, V = 2) ) CVsl <- CV_lrnr_sl( lrnr_sl = sl_fit, task = washb_task_new, loss_fun = loss_squared_error ) CVsl %>% kable(digits = 4) %>% kableExtra:::kable_styling(fixed_thead = T) %>% scroll_box(width = "100%", height = "300px") learner coefficients risk se fold_sd fold_min_risk fold_max_risk glm 0.0556 1.0340 0.0255 0.0195 1.0203 1.0478 polspline 0.0556 1.0231 0.0236 0.0005 1.0227 1.0235 enet.5 0.0556 1.0140 0.0236 0.0081 1.0083 1.0197 ridge 0.0556 1.0192 0.0239 0.0118 1.0108 1.0275 lasso 0.0556 1.0141 0.0236 0.0081 1.0084 1.0198 xgboost50 0.0556 1.1647 0.0259 0.0025 1.1629 1.1665 randomforest_screen_glm 0.0556 1.0245 0.0239 0.0222 1.0088 1.0403 randomforest_screen_polspline 0.0555 1.0315 0.0239 0.0231 1.0151 1.0478 randomforest_screen_enet.5 0.0556 1.0240 0.0238 0.0214 1.0089 1.0392 randomforest_screen_ridge 0.0556 1.0243 0.0239 0.0218 1.0089 1.0397 randomforest_screen_lasso 0.0556 1.0240 0.0238 0.0214 1.0089 1.0392 randomforest_screen_xgboost50 0.0555 1.1798 0.0270 0.0522 1.1430 1.2167 lasso_screen_glm 0.0556 1.0233 0.0238 0.0001 1.0232 1.0234 lasso_screen_polspline 0.0556 1.0247 0.0238 0.0018 1.0235 1.0260 lasso_screen_enet.5 0.0556 1.0233 0.0238 0.0001 1.0232 1.0233 lasso_screen_ridge 0.0556 1.0233 0.0238 0.0002 1.0231 1.0235 lasso_screen_lasso 0.0556 1.0233 0.0238 0.0001 1.0232 1.0233 lasso_screen_xgboost50 0.0556 1.1265 0.0253 0.0263 1.1079 1.1451 SuperLearner NA 1.0144 0.0236 0.0086 1.0083 1.0204 ## Variable Importance Measures with sl3 Variable importance can be interesting and informative. It can also be contradictory and confusing. Nevertheless, we like it, and so do our collaborators, so we created a variable importance function in sl3! The sl3 importance function returns a table with variables listed in decreasing order of importance (i.e., most important on the first row). The measure of importance in sl3 is based on a risk ratio, or risk difference, between the learner fit with a removed, or permuted, covariate and the learner fit with the true covariate, across all covariates. In this manner, the larger the risk difference, the more important the variable is in the prediction. The intuition of this measure is that it calculates the risk (in terms of the average loss in predictive accuracy) of losing one covariate, while keeping everything else fixed, and compares it to the risk if the covariate was not lost. If this risk ratio is one, or risk difference is zero, then losing that covariate had no impact, and is thus not important by this measure. We do this across all of the covariates. As stated above, we can remove the covariate and refit the SL without it, or we just permute the covariate (faster) and hope for the shuffling to distort any meaningful information that was present in the covariate. This idea of permuting instead of removing saves a lot of time, and is also incorporated in the randomForest variable importance measures. However, the permutation approach is risky, so the importance function default is to remove and refit. Let’s explore the sl3 variable importance measurements for the washb data. washb_varimp <- importance(sl_fit, loss = loss_squared_error, type = "permute") washb_varimp %>% kable(digits = 4) %>% kableExtra:::kable_styling(fixed_thead = TRUE) %>% scroll_box(width = "100%", height = "300px") X risk_ratio aged 1.0408 momedu 1.0125 asset_refrig 1.0084 asset_chair 1.0044 month 1.0032 elec 1.0020 tr 1.0017 momheight 1.0013 Nlt18 1.0010 momage 1.0008 floor 1.0006 asset_chouki 1.0003 asset_mobile 1.0003 asset_moto 1.0002 delta_momheight 1.0001 watmin 1.0000 asset_table 1.0000 sex 1.0000 walls 1.0000 asset_tv 1.0000 delta_momage 1.0000 roof 0.9999 Ncomp 0.9999 hfiacat 0.9999 fracode 0.9999 asset_wardrobe 0.9998 asset_sewmach 0.9998 asset_bike 0.9998 asset_khat 0.9996 # plot variable importance importance_plot( washb_varimp, main = "sl3 Variable Importance for WASH Benefits Example Data" ) ## 6.2 Exercises ### 6.2.1 Predicting Myocardial Infarction with sl3 Follow the steps below to predict myocardial infarction (mi) using the available covariate data. We thank Prof. David Benkeser at Emory University for making the this Cardiovascular Health Study (CHS) data accessible. # load the data set db_data <- url( paste0( "https://raw.githubusercontent.com/benkeser/sllecture/master/", "chspred.csv" ) ) chspred <- read_csv(file = db_data, col_names = TRUE) Let’s take a quick peek at the data: waist alcoh hdl beta smoke ace ldl bmi aspirin gend age estrgn glu ins cysgfr dm fetuina whr hsed race logcystat logtrig logcrp logcre health logkcal sysbp mi 110.164 0.0000 66.497 0 0 1 114.216 27.997 0 0 73.518 0 159.931 70.3343 75.008 1 0.17516 1.16898 1 1 -0.34202 5.4063 2.01260 -0.67385 0 4.3926 177.135 0 89.976 0.0000 50.065 0 0 0 103.777 20.893 0 0 61.772 0 153.389 33.9695 82.743 1 0.57165 0.90114 0 0 -0.08465 4.8592 3.29328 -0.55509 1 6.2071 136.374 0 106.194 8.4174 40.506 0 0 0 165.716 28.455 1 1 72.931 0 121.715 -17.3017 74.699 0 0.35168 1.17971 0 1 -0.44511 4.5088 0.30132 -0.01152 0 6.7320 135.199 0 90.057 0.0000 36.175 0 0 0 45.203 23.961 0 0 79.119 0 53.969 11.7315 95.782 0 0.54391 1.13599 0 0 -0.48072 5.1832 3.02426 -0.57507 1 7.3972 139.018 0 78.614 2.9790 71.064 0 1 0 131.312 10.966 0 1 69.018 0 94.315 9.7112 72.711 0 0.49159 1.10276 1 0 0.31206 4.2190 -0.70568 0.00534 1 8.2779 88.047 0 91.659 0.0000 59.496 0 0 0 171.187 29.132 0 1 81.835 0 212.907 -28.2269 69.218 1 0.46215 0.95291 1 0 -0.28716 5.1773 0.97046 0.21268 1 5.9942 69.594 0 1. Create an sl3 task, setting myocardial infarction mi as the outcome and using all available covariate data. 2. Make a library of seven relatively fast base learning algorithms (i.e., do not consider BART or HAL). Customize hyperparameters for one of your learners. Feel free to use learners from sl3 or SuperLearner. You may use the same base learning library that is presented above. 3. Incorporate at least one pipeline with feature selection. Any screener and learner(s) can be used. 4. Fit the metalearning step with the default metalearner. 5. With the metalearner and base learners, make the Super Learner (SL) and train it on the task. 6. Print the SL fit results by adding $cv_risk(loss_squared_error) to your fit object. The squared error loss is specified here, since that’s what is used by the default metalearner.
7. Cross-validate your SL fit to see how well it performs on unseen data. Specify the loss_squared_error loss function to evaluate the SL as it’s the same loss that was used by the default metalearner. Print the result.
8. Use the importance() function to identify the “most important” predictor of myocardial infarction, according to sl3 importance metrics. Print the result.

## 6.3 Concluding Remarks

• Super Learner (SL) is a general approach that can be applied to a diversity of estimation and prediction problems which can be defined by a loss function.

• It would be straightforward to plug in the estimator returned by SL into the target parameter mapping.

• For example, suppose we are after the average treatment effect (ATE) of a binary treatment intervention: $$\Psi_0 = E_{0,W}[E_0(Y|A=1,W) - E_0(Y|A=0,W)]$$.
• We could use the SL that was trained on the original data (let’s call this sl_fit) to predict the outcome for all subjects under each intervention. All we would need to do is take the average difference between the counterfactual outcomes under each intervention of interest.
• Considering $$\Psi_0$$ above, we would first need two $$n$$-length vectors of predicted outcomes under each intervention. One vector would represent the predicted outcomes under an intervention that sets all subjects to receive $$A=1$$, $$Y_i|A_i=1,W_i$$ for all $$i=1,\ldots,n$$. The other vector would represent the predicted outcomes under an intervention that sets all subjects to receive $$A=0$$, $$Y_i|A_i=0,W_i$$ for all $$i=1,\ldots,n$$.
• After obtaining these vectors of counterfactual predicted outcomes, all we would need to do is average and then take the difference in order to “plug-in” the SL estimator into the target parameter mapping.
• In sl3 and with our current ATE example, this could be achieved with mean(sl_fit$predict(A1_task)) - mean(sl_fit$predict(A0_task)); where A1_task$data would contain all 1’s (or the level that pertains to receiving the treatment) for the treatment column in the data (keeping all else the same), and A0_task$data would contain all 0’s (or the level that pertains to not receiving the treatment) for the treatment column in the data.
• It’s a worthwhile exercise to obtain the predicted counterfactual outcomes and create these counterfactual sl3 tasks. It’s too biased; however, to plug the SL fit into the target parameter mapping, (e.g., calling the result of mean(sl_fit$predict(A1_task)) - mean(sl_fit$predict(A0_task)) the estimated ATE. We would end up with an estimator for the ATE that was optimized for estimation of the prediction function, and not the ATE!

• At the end of the “analysis day”, we want an estimator that is optimized for our target estimand of interest. We ultimately care about doing a good job estimating $$\psi_0$$. The SL is an essential step to help us get there. In fact, we will use the counterfactual predicted outcomes that were explained at length above. However, SL is not the end of the estimation procedure. Specifically, the Super Learner would not be an asymptotically linear estimator of the target estimand; and it is not an efficient substitution estimator. This begs the question, why is it so important for an estimator to possess these properties?

• An asymptotically linear estimator converges to the estimand a $$\frac{1}{\sqrt{n}}$$ rate, thereby permitting formal statistical inference (i.e., confidence intervals and $$p$$-values) [ADD REF].

• Substitution, or plug-in, estimators of the estimand are desirable because they respect both the local and global constraints of the statistical model (e.g., bounds), and have they have better finite-sample properties[ADD REF].

• An efficient estimator is optimal in the sense that it has the lowest possible variance, and is thus the most precise. An estimator is efficient if and only if is asymptotically linear with influence curve equal to the canonical gradient [ADD REF].

• The canonical gradient is a mathematical object that is specific to the target estimand, and it provides information on the level of difficulty of the estimation problem [ADD REF]. Various canonical gradient are shown in the chapters that follow.
• Practitioner’s do not need to know how to calculate a canonical gradient in order to understand efficiency and use Targeted Maximum Likelihood Estimation (TMLE). Metaphorically, you do not need to be Yoda in order to be a Jedi.
• TMLE is a general strategy that succeeds in constructing efficient and asymptotically linear plug-in estimators.

• SL is fantastic for pure prediction, and for obtaining an initial estimate in the first step of TMLE, but we need the second step of TMLE to have the desirable statistical properties mentioned above.

• In the chapters that follow, we focus on the targeted maximum likelihood estimator and the targeted minimum loss-based estimator, both referred to as TMLE.

## 6.4 Appendix

### 6.4.1 Exercise 1 Solution

Here is a potential solution to the sl3 Exercise 1 – Predicting Myocardial Infarction with sl3.

db_data <- url(
"https://raw.githubusercontent.com/benkeser/sllecture/master/chspred.csv"
)
chspred <- read_csv(file = db_data, col_names = TRUE)
data.table::setDT(chspred)

data = chspred,
covariates = colnames(chspred)[-1],
outcome = "mi"
)

# make learners
glm_learner <- Lrnr_glm$new() lasso_learner <- Lrnr_glmnet$new(alpha = 1)
ridge_learner <- Lrnr_glmnet$new(alpha = 0) enet_learner <- Lrnr_glmnet$new(alpha = 0.5)
# curated_glm_learner uses formula = "mi ~ smoke + beta"
curated_glm_learner <- Lrnr_glm_fast$new(covariates = c("smoke", "beta")) mean_learner <- Lrnr_mean$new() # That is one mean learner!
glm_fast_learner <- Lrnr_glm_fast$new() ranger_learner <- Lrnr_ranger$new()
svm_learner <- Lrnr_svm$new() xgb_learner <- Lrnr_xgboost$new()

# screening
screen_cor <- make_learner(Lrnr_screener_correlation)
glm_pipeline <- make_learner(Pipeline, screen_cor, glm_learner)

# stack learners together
stack <- make_learner(
Stack,
glm_pipeline, glm_learner,
lasso_learner, ridge_learner, enet_learner,
curated_glm_learner, mean_learner, glm_fast_learner,
ranger_learner, svm_learner, xgb_learner
)

# make and train SL
sl <- Lrnr_sl$new( learners = stack ) sl_fit <- sl$train(chspred_task)
sl_fit$cv_risk(loss_squared_error) CVsl <- CV_lrnr_sl(sl_fit, chspred_task, loss_squared_error) CVsl varimp <- importance(sl_fit) importance_plot(varimp)  ### 6.4.2 Exercise 2 Solution Here is a potential solution to sl3 Exercise 2 – Predicting Recurrent Ischemic Stroke in an RCT with sl3. library(ROCR) # for AUC calculation ist_data <- paste0( "https://raw.githubusercontent.com/tlverse/", "tlverse-handbook/master/data/ist_sample.csv" ) %>% fread() # stack ist_task <- make_sl3_Task( data = ist_data, outcome = "DRSISC", covariates = colnames(ist_data)[-which(names(ist_data) == "DRSISC")], drop_missing_outcome = TRUE ) # learner library lrn_glm <- Lrnr_glm$new()
lrn_lasso <- Lrnr_glmnet$new(alpha = 1) lrn_ridge <- Lrnr_glmnet$new(alpha = 0)
lrn_enet <- Lrnr_glmnet$new(alpha = 0.5) lrn_mean <- Lrnr_mean$new()
lrn_ranger <- Lrnr_ranger$new() lrn_svm <- Lrnr_svm$new()
# xgboost grid
grid_params <- list(
max_depth = c(2, 5, 8),
eta = c(0.01, 0.15, 0.3)
)
grid <- expand.grid(grid_params, KEEP.OUT.ATTRS = FALSE)
xgb_learners <- apply(grid, MARGIN = 1, function(params_tune) {
do.call(Lrnr_xgboost$new, as.list(params_tune)) }) learners <- unlist(list( xgb_learners, lrn_ridge, lrn_mean, lrn_lasso, lrn_glm, lrn_enet, lrn_ranger, lrn_svm ), recursive = TRUE ) # SL sl <- Lrnr_sl$new(learners)
sl_fit <- sl$train(ist_task) # AUC preds <- sl_fit$predict()
obs <- c(na.omit(ist_data$DRSISC)) AUC <- performance(prediction(sl_preds, obs), measure = "auc")@y.values[[1]] plot(performance(prediction(sl_preds, obs), "tpr", "fpr")) # CVsl ist_task_CVsl <- make_sl3_Task( data = ist_data, outcome = "DRSISC", covariates = colnames(ist_data)[-which(names(ist_data) == "DRSISC")], drop_missing_outcome = TRUE, folds = origami::make_folds( n = sum(!is.na(ist_data$DRSISC)),
fold_fun = folds_vfold,
V = 5
)
)
CVsl

# sl3 variable importance plot
ist_varimp <- importance(sl_fit, type = "permute")
ist_varimp %>%
importance_plot(
main = "Variable Importance for Predicting Recurrent Ischemic Stroke"
)