---
title: "03. Ensemble vs. Best Model Selection"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{03. Ensemble vs. Best Model Selection}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5
)
has_rpart <- requireNamespace("rpart", quietly = TRUE)
```

## Introduction

In the theoretical framework of Super Learning, there are two distinct ways to utilize the cross-validated risks of your base algorithms:

1. **The Ensemble Super Learner:** Calculates a weighted average (convex combination) of the base learners. This "soft selection" smooths out variance, integrates different feature spaces, and generally yields the lowest finite-sample prediction error.
2. **The "Best" Model Selector:** Identifies the single algorithm with the lowest cross-validated risk and assigns it a weight of `1.0` (and all others `0.0`). This is a "hard selection" or "winner-take-all" approach. 



While the Ensemble is asymptotically optimal, selecting the single best model is incredibly useful when interpretability is strictly tied to one specific algorithm family. Instead of manually cherry-picking the best model—which introduces researcher bias and invalidates post-selection inference—`SuperSurv` automates the selection using rigorous, internal cross-validation. 

You can easily toggle between these two paradigms using the `selection` argument.

## 1. Prepare the Data

We load the `metabric` dataset and define our evaluation time grid exactly as we did in the previous tutorials.

```{r setup, message=FALSE, warning=FALSE}
library(SuperSurv)
library(survival)

data("metabric", package = "SuperSurv")
set.seed(42)

# Standard Train/Test Split
train_idx <- sample(1:nrow(metabric), 0.7 * nrow(metabric))
train <- metabric[train_idx, ]
test  <- metabric[-train_idx, ]

X_tr <- train[, grep("^x", names(metabric))]
X_te <- test[, grep("^x", names(metabric))]
new.times <- seq(50, 200, by = 25)

# Define a diverse library of base learners
my_library <- c("surv.coxph", "surv.weibull")
if (has_rpart) {
  my_library <- c(my_library, "surv.rpart")
}
```

## 2. Fit Both Super Learners

We will fit two separate `SuperSurv` models. The first will use the default Ensemble approach (`selection = "ensemble"`), and the second will use the winner-take-all approach (`selection = "best"`).

```{r fit-models, results='hide', message=FALSE, warning=FALSE}
# 1. The Ensemble Super Learner (Weighted Average)
fit_ensemble <- SuperSurv(
  time = train$duration,
  event = train$event,
  X = X_tr,
  newdata = X_te,
  new.times = new.times,
  event.library = my_library,
  cens.library = my_library,
  control = list(saveFitLibrary = TRUE),
  verbose = FALSE,
  selection = "ensemble",     # <-- Calculates fractional weights
  nFolds = 3
)

# 2. The 'Best' Super Learner (Winner-Take-All)
fit_best <- SuperSurv(
  time = train$duration,
  event = train$event,
  X = X_tr,
  newdata = X_te,
  new.times = new.times,
  event.library = my_library,
  cens.library = my_library,
  control = list(saveFitLibrary = TRUE),
  verbose = FALSE,
  selection = "best",         # <-- Selects the single best model
  nFolds = 3
)
```

## 3. Inspecting the Weights

The difference between the two methodologies is immediately obvious when we inspect the meta-learner coefficients with `event_weights()`. 

```{r inspect-weights}
cat("\n--- ENSEMBLE WEIGHTS (selection = 'ensemble') ---\n")
print(round(event_weights(fit_ensemble), 4))

cat("\n--- BEST MODEL WEIGHTS (selection = 'best') ---\n")
print(round(event_weights(fit_best), 4))
```

**Interpretation:** Notice that the Ensemble Super Learner distributes the weight across multiple models to minimize the overall loss function. The "Best" Super Learner simply looks at the cross-validated risk, identifies the champion, and gives it 100% of the weight. 

## 4. Evaluate and Compare Performance

A common question in clinical research is: *"Does the complexity of the Ensemble actually perform better than simply picking the best single model?"*

We can answer this instantly using `eval_summary()`. 

```{r evaluate-comparison}
# Evaluate the Ensemble
cat("Performance of the ENSEMBLE Super Learner:\n")
eval_summary(fit_ensemble, newdata = X_te, time = test$duration, 
             event = test$event, eval_times = new.times)

cat("\nPerformance of the BEST MODEL Super Learner:\n")
eval_summary(fit_best, newdata = X_te, time = test$duration, 
             event = test$event, eval_times = new.times)
```

By comparing the resulting Brier scores and C-indices, you can empirically justify whether the "soft selection" of the ensemble is mathematically necessary for your specific dataset, or if the "hard selection" of a single model is sufficient.
