我试图解决医学中的一个常见问题：预测模型与其他来源的结合，例如，专家意见 [有时在医学中非常强调]，在这篇文章中称为superdoc预测器。
这可以通过将模型与逻辑回归（输入专家意见）堆叠起来来解决，如本文第 26 页所述：
Afshar P, Mohammadi A, Plataniotis KN, Oikonomou A, Benali H. 从手工制作到基于深度学习的癌症放射组学：挑战和机遇。IEEE 信号处理杂志 2019；36：132-60。在这里可用
我在这里尝试过，没有考虑过拟合（我没有应用低级学习者的非折叠预测）：
示例数据
# library
library(tidyverse)
library(caret)
library(glmnet)
library(mlbench)
# get example data
data(PimaIndiansDiabetes, package="mlbench")
data <- PimaIndiansDiabetes
# add the super doctors opinion to the data
set.seed(2323)
data %>%
rowwise() %>%
mutate(superdoc=case_when(diabetes=="pos" ~ as.numeric(sample(0:2,1)), TRUE~ 0)) -> data
# separate the data in a training set and test set
train.data <- data[1:550,]
test.data <- data[551:768,]
不考虑折叠预测的堆叠模型：
# elastic net regression (without the superdoc's opinion)
set.seed(2323)
model <- train(
diabetes ~., data = train.data %>% select(-superdoc), method = "glmnet",
trControl = trainControl("repeatedcv",
number = 10,
repeats=10,
classProbs = TRUE,
savePredictions = TRUE,
summaryFunction = twoClassSummary),
tuneLength = 10,
metric="ROC" #ROC metric is in twoClassSummary
)
# extract the coefficients for the best alpha and lambda
coef(model$finalModel, model$finalModel$lambdaOpt) -> coeffs
tidy(coeffs) %>% tibble() -> coeffs
coef.interc = coeffs %>% filter(row=="(Intercept)") %>% pull(value)
coef.pregnant = coeffs %>% filter(row=="pregnant") %>% pull(value)
coef.glucose = coeffs %>% filter(row=="glucose") %>% pull(value)
coef.pressure = coeffs %>% filter(row=="pressure") %>% pull(value)
coef.mass = coeffs %>% filter(row=="mass") %>% pull(value)
coef.pedigree = coeffs %>% filter(row=="pedigree") %>% pull(value)
coef.age = coeffs %>% filter(row=="age") %>% pull(value)
# combine the model with the superdoc's opinion in a logistic regression model
finalmodel = glm(diabetes ~ superdoc + I(coef.interc + coef.pregnant*pregnant + coef.glucose*glucose + coef.pressure*pressure + coef.mass*mass + coef.pedigree*pedigree + coef.age*age),family=binomial, data=train.data)
# make predictions on the test data
predict(finalmodel,test.data, type="response") -> predictions
# check the AUC of the model in the test data
roc(test.data$diabetes,predictions, ci=TRUE)
#> Setting levels: control = neg, case = pos
#> Setting direction: controls < cases
#>
#> Call:
#> roc.default(response = test.data$diabetes, predictor = predictions, ci = TRUE)
#>
#> Data: predictions in 145 controls (test.data$diabetes neg) < 73 cases (test.data$diabetes pos).
#> Area under the curve: 0.9345
#> 95% CI: 0.8969-0.9721 (DeLong)
现在我想mlr3根据这篇非常有用的帖子来考虑使用包系列的非折叠预测：调整堆叠学习器
#library
library(mlr3)
library(mlr3learners)
library(mlr3pipelines)
library(mlr3filters)
library(mlr3tuning)
library(paradox)
library(glmnet)
# creat elastic net regression
glmnet_lrn = lrn("classif.cv_glmnet", predict_type = "prob")
# create the learner out-of-bag predictions
glmnet_cv1 = po("learner_cv", glmnet_lrn, id = "glmnet") #I could not find a setting to filter the predictors (ie, not send the superdoc predictor here)
# summarize steps
level0 = gunion(list(
glmnet_cv1,
po("nop", id = "only_superdoc_predictor"))) %>>% #I could not find a setting to send only the superdoc predictor to "union1"
po("featureunion", id = "union1")
# final logistic regression
log_reg_lrn = lrn("classif.log_reg", predict_type = "prob")
# combine ensemble model
ensemble = level0 %>>% log_reg_lrn
ensemble$plot(html = FALSE)
由reprex 包于 2021-03-15 创建(v1.0.0)
我的问题（我对mlr3包系列比较陌生）
- 包装系列是否
mlr3非常适合我尝试构建的集成模型？ - 如果是，我最终确定集成模型并预测
test.data
