sbf() 是否使用度量参数来优化模型？
Does sbf() use metric argument to optimize model?
将 ROC 作为 metric 参数值传递给 caretSBF 函数
我们的 objective 是使用 ROC 摘要指标进行模型选择，同时 运行 通过过滤选择 sbf() 功能进行特征选择。
BreastCancer 数据集被用作从 mlbench 包到 运行 train() 和 sbf() 以及 metric = "Accuracy" 和 metric = "ROC"
我们要确保 sbf() 采用 metric 参数，正如 train() 和 rfe() 函数所应用的那样，以优化模型。为此，我们计划将 train() 函数与 sbf() 函数结合使用。 caretSBF$fit 函数调用 train()，并且 caretSBF 被传递给 sbfControl。
从输出来看，似乎 metric 参数仅用于 inner resampling 而不是用于 sbf 部分，即输出的 outer resampling， metric 参数未像 train() 和 rfe() 所使用的那样应用。
因为我们已经使用了 caretSBF，它使用 train()，看来 metric 参数的范围限于 train()，因此不会传递给 sbf。
我们希望澄清 sbf() 是否使用 metric 参数来优化模型，即 outer resampling?
这是我们关于可重现示例的工作，显示 train() 使用 metric 参数使用 Accuracy 和 ROC，但对于 sbf 我们不确定.
我。数据部分
## Loading required packages
library(mlbench)
library(caret)
## Loading `BreastCancer` Dataset from *mlbench* package
data("BreastCancer")
## Data cleaning for missing values
# Remove rows/observation with NA Values in any of the columns
BrC1 <- BreastCancer[complete.cases(BreastCancer),]
# Removing Class and Id Column and keeping just Numeric Predictors
Num_Pred <- BrC1[,2:10]
二．自定义汇总函数
定义 fiveStats 汇总函数
fiveStats <- function(...) c(twoClassSummary(...),
defaultSummary(...))
三．火车区间
定义 trControl
trCtrl <- trainControl(method="repeatedcv", number=10,
repeats=1, classProbs = TRUE, summaryFunction = fiveStats)
火车 + 公制 = "Accuracy"
set.seed(1)
TR_acc <- train(Num_Pred,BrC1$Class, method="rf",metric="Accuracy",
trControl = trCtrl,tuneGrid=expand.grid(.mtry=c(2,3,4,5)))
TR_acc
# Random Forest
#
# 683 samples
# 9 predictor
# 2 classes: 'benign', 'malignant'
#
# No pre-processing
# Resampling: Cross-Validated (10 fold, repeated 1 times)
# Summary of sample sizes: 615, 615, 614, 614, 614, 615, ...
# Resampling results across tuning parameters:
#
# mtry ROC Sens Spec Accuracy Kappa
# 2 0.9936532 0.9729798 0.9833333 0.9765772 0.9490311
# 3 0.9936544 0.9729293 0.9791667 0.9750853 0.9457534
# 4 0.9929957 0.9684343 0.9750000 0.9706948 0.9361373
# 5 0.9922907 0.9684343 0.9666667 0.9677536 0.9295782
#
# Accuracy was used to select the optimal model using the largest value.
# The final value used for the model was mtry = 2.
火车 + 公制 = "ROC"
set.seed(1)
TR_roc <- train(Num_Pred,BrC1$Class, method="rf",metric="ROC",
trControl = trCtrl,tuneGrid=expand.grid(.mtry=c(2,3,4,5)))
TR_roc
# Random Forest
#
# 683 samples
# 9 predictor
# 2 classes: 'benign', 'malignant'
#
# No pre-processing
# Resampling: Cross-Validated (10 fold, repeated 1 times)
# Summary of sample sizes: 615, 615, 614, 614, 614, 615, ...
# Resampling results across tuning parameters:
#
# mtry ROC Sens Spec Accuracy Kappa
# 2 0.9936532 0.9729798 0.9833333 0.9765772 0.9490311
# 3 0.9936544 0.9729293 0.9791667 0.9750853 0.9457534
# 4 0.9929957 0.9684343 0.9750000 0.9706948 0.9361373
# 5 0.9922907 0.9684343 0.9666667 0.9677536 0.9295782
#
# ROC was used to select the optimal model using the largest value.
# The final value used for the model was mtry = 3.
四．编辑 caretSBF
编辑 caretSBF 摘要函数
caretSBF$summary <- fiveStats
V。 SBF 部分
正在定义 sbfControl
sbfCtrl <- sbfControl(functions=caretSBF,
method="repeatedcv", number=10, repeats=1,
verbose=T, saveDetails = T)
SBF + 公制 = "Accuracy"
set.seed(1)
sbf_acc <- sbf(Num_Pred, BrC1$Class,
sbfControl = sbfCtrl,
trControl = trCtrl, method="rf", metric="Accuracy")
## sbf_acc
sbf_acc
# Selection By Filter
#
# Outer resampling method: Cross-Validated (10 fold, repeated 1 times)
#
# Resampling performance:
#
# ROC Sens Spec Accuracy Kappa ROCSD SensSD SpecSD AccuracySD KappaSD
# 0.9931 0.973 0.9833 0.9766 0.949 0.006272 0.0231 0.02913 0.01226 0.02646
#
# Using the training set, 9 variables were selected:
# Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, Epith.c.size...
#
# During resampling, the top 5 selected variables (out of a possible 9):
# Bare.nuclei (100%), Bl.cromatin (100%), Cell.shape (100%), Cell.size (100%), Cl.thickness (100%)
#
# On average, 9 variables were selected (min = 9, max = 9)
## Class of sbf_acc
class(sbf_acc)
# [1] "sbf"
## Names of elements of sbf_acc
names(sbf_acc)
# [1] "pred" "variables" "results" "fit" "optVariables"
# [6] "call" "control" "resample" "metrics" "times"
# [11] "resampledCM" "obsLevels" "dots"
## sbf_acc fit element*
sbf_acc$fit
# Random Forest
#
# 683 samples
# 9 predictor
# 2 classes: 'benign', 'malignant'
#
# No pre-processing
# Resampling: Cross-Validated (10 fold, repeated 1 times)
# Summary of sample sizes: 615, 614, 614, 615, 615, 615, ...
# Resampling results across tuning parameters:
#
# mtry ROC Sens Spec Accuracy Kappa
# 2 0.9933176 0.9706566 0.9833333 0.9751492 0.9460717
# 5 0.9920034 0.9662121 0.9791667 0.9707801 0.9363708
# 9 0.9914825 0.9684343 0.9708333 0.9693308 0.9327662
#
# Accuracy was used to select the optimal model using the largest value.
# The final value used for the model was mtry = 2.
## Elements of sbf_acc fit
names(sbf_acc$fit)
# [1] "method" "modelInfo" "modelType" "results" "pred"
# [6] "bestTune" "call" "dots" "metric" "control"
# [11] "finalModel" "preProcess" "trainingData" "resample" "resampledCM"
# [16] "perfNames" "maximize" "yLimits" "times" "levels"
## sbf_acc fit final Model
sbf_acc$fit$finalModel
# Call:
# randomForest(x = x, y = y, mtry = param$mtry)
# Type of random forest: classification
# Number of trees: 500
# No. of variables tried at each split: 2
#
# OOB estimate of error rate: 2.34%
# Confusion matrix:
# benign malignant class.error
# benign 431 13 0.02927928
# malignant 3 236 0.01255230
## sbf_acc metric
sbf_acc$fit$metric
# [1] "Accuracy"
## sbf_acc fit best Tune*
sbf_acc$fit$bestTune
# mtry
# 1 2
SBF + 公制 = "ROC"
set.seed(1)
sbf_roc <- sbf(Num_Pred, BrC1$Class,
sbfControl = sbfCtrl,
trControl = trCtrl, method="rf", metric="ROC")
## sbf_roc
sbf_roc
# Selection By Filter
#
# Outer resampling method: Cross-Validated (10 fold, repeated 1 times)
#
# Resampling performance:
#
# ROC Sens Spec Accuracy Kappa ROCSD SensSD SpecSD AccuracySD KappaSD
# 0.9931 0.973 0.9833 0.9766 0.949 0.006272 0.0231 0.02913 0.01226 0.02646
#
# Using the training set, 9 variables were selected:
# Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, Epith.c.size...
#
# During resampling, the top 5 selected variables (out of a possible 9):
# Bare.nuclei (100%), Bl.cromatin (100%), Cell.shape (100%), Cell.size (100%), Cl.thickness (100%)
#
# On average, 9 variables were selected (min = 9, max = 9)
## Class of sbf_roc
class(sbf_roc)
# [1] "sbf"
## Names of elements of sbf_roc
names(sbf_roc)
# [1] "pred" "variables" "results" "fit" "optVariables"
# [6] "call" "control" "resample" "metrics" "times"
# [11] "resampledCM" "obsLevels" "dots"
## sbf_roc fit element*
sbf_roc$fit
# Random Forest
#
# 683 samples
# 9 predictor
# 2 classes: 'benign', 'malignant'
#
# No pre-processing
# Resampling: Cross-Validated (10 fold, repeated 1 times)
# Summary of sample sizes: 615, 614, 614, 615, 615, 615, ...
# Resampling results across tuning parameters:
#
# mtry ROC Sens Spec Accuracy Kappa
# 2 0.9933176 0.9706566 0.9833333 0.9751492 0.9460717
# 5 0.9920034 0.9662121 0.9791667 0.9707801 0.9363708
# 9 0.9914825 0.9684343 0.9708333 0.9693308 0.9327662
#
# ROC was used to select the optimal model using the largest value.
# The final value used for the model was mtry = 2.
## Elements of sbf_roc fit
names(sbf_roc$fit)
# [1] "method" "modelInfo" "modelType" "results" "pred"
# [6] "bestTune" "call" "dots" "metric" "control"
# [11] "finalModel" "preProcess" "trainingData" "resample" "resampledCM"
# [16] "perfNames" "maximize" "yLimits" "times" "levels"
## sbf_roc fit final Model
sbf_roc$fit$finalModel
# Call:
# randomForest(x = x, y = y, mtry = param$mtry)
# Type of random forest: classification
# Number of trees: 500
# No. of variables tried at each split: 2
#
# OOB estimate of error rate: 2.34%
# Confusion matrix:
# benign malignant class.error
# benign 431 13 0.02927928
# malignant 3 236 0.01255230
## sbf_roc metric
sbf_roc$fit$metric
# [1] "ROC"
## sbf_roc fit best Tune
sbf_roc$fit$bestTune
# mtry
# 1 2
sbf() 是否使用 metric 参数来优化模型？如果是，metric sbf() 默认使用什么？如果 sbf() 使用 metric 参数，那么如何将其设置为 ROC?
谢谢。
sbf 不使用度量来优化任何东西（不像 rfe）； sbf 所做的只是在调用模型之前执行特征选择步骤。当然，您定义了过滤器，但无法使用 sbf 调整过滤器，因此不需要指标来指导该步骤。
使用 sbf(x, y, metric = "ROC") 会将 metric = "ROC" 传递给您正在使用的任何建模函数（并且它设计为在使用 caretSBF 时与 train 一起使用。发生这种情况是因为sbf:
没有 metric 参数
> names(formals(caret:::sbf.default))
[1] "x" "y" "sbfControl" "..."
将 ROC 作为 metric 参数值传递给 caretSBF 函数
我们的 objective 是使用 ROC 摘要指标进行模型选择，同时 运行 通过过滤选择 sbf() 功能进行特征选择。
BreastCancer 数据集被用作从 mlbench 包到 运行 train() 和 sbf() 以及 metric = "Accuracy" 和 metric = "ROC"
我们要确保 sbf() 采用 metric 参数，正如 train() 和 rfe() 函数所应用的那样，以优化模型。为此，我们计划将 train() 函数与 sbf() 函数结合使用。 caretSBF$fit 函数调用 train()，并且 caretSBF 被传递给 sbfControl。
从输出来看，似乎 metric 参数仅用于 inner resampling 而不是用于 sbf 部分，即输出的 outer resampling， metric 参数未像 train() 和 rfe() 所使用的那样应用。
因为我们已经使用了 caretSBF，它使用 train()，看来 metric 参数的范围限于 train()，因此不会传递给 sbf。
我们希望澄清 sbf() 是否使用 metric 参数来优化模型，即 outer resampling?
这是我们关于可重现示例的工作，显示 train() 使用 metric 参数使用 Accuracy 和 ROC，但对于 sbf 我们不确定.
我。数据部分
## Loading required packages
library(mlbench)
library(caret)
## Loading `BreastCancer` Dataset from *mlbench* package
data("BreastCancer")
## Data cleaning for missing values
# Remove rows/observation with NA Values in any of the columns
BrC1 <- BreastCancer[complete.cases(BreastCancer),]
# Removing Class and Id Column and keeping just Numeric Predictors
Num_Pred <- BrC1[,2:10]
二．自定义汇总函数
定义 fiveStats 汇总函数
fiveStats <- function(...) c(twoClassSummary(...),
defaultSummary(...))
三．火车区间
定义 trControl
trCtrl <- trainControl(method="repeatedcv", number=10,
repeats=1, classProbs = TRUE, summaryFunction = fiveStats)
火车 + 公制 = "Accuracy"
set.seed(1)
TR_acc <- train(Num_Pred,BrC1$Class, method="rf",metric="Accuracy",
trControl = trCtrl,tuneGrid=expand.grid(.mtry=c(2,3,4,5)))
TR_acc
# Random Forest
#
# 683 samples
# 9 predictor
# 2 classes: 'benign', 'malignant'
#
# No pre-processing
# Resampling: Cross-Validated (10 fold, repeated 1 times)
# Summary of sample sizes: 615, 615, 614, 614, 614, 615, ...
# Resampling results across tuning parameters:
#
# mtry ROC Sens Spec Accuracy Kappa
# 2 0.9936532 0.9729798 0.9833333 0.9765772 0.9490311
# 3 0.9936544 0.9729293 0.9791667 0.9750853 0.9457534
# 4 0.9929957 0.9684343 0.9750000 0.9706948 0.9361373
# 5 0.9922907 0.9684343 0.9666667 0.9677536 0.9295782
#
# Accuracy was used to select the optimal model using the largest value.
# The final value used for the model was mtry = 2.
火车 + 公制 = "ROC"
set.seed(1)
TR_roc <- train(Num_Pred,BrC1$Class, method="rf",metric="ROC",
trControl = trCtrl,tuneGrid=expand.grid(.mtry=c(2,3,4,5)))
TR_roc
# Random Forest
#
# 683 samples
# 9 predictor
# 2 classes: 'benign', 'malignant'
#
# No pre-processing
# Resampling: Cross-Validated (10 fold, repeated 1 times)
# Summary of sample sizes: 615, 615, 614, 614, 614, 615, ...
# Resampling results across tuning parameters:
#
# mtry ROC Sens Spec Accuracy Kappa
# 2 0.9936532 0.9729798 0.9833333 0.9765772 0.9490311
# 3 0.9936544 0.9729293 0.9791667 0.9750853 0.9457534
# 4 0.9929957 0.9684343 0.9750000 0.9706948 0.9361373
# 5 0.9922907 0.9684343 0.9666667 0.9677536 0.9295782
#
# ROC was used to select the optimal model using the largest value.
# The final value used for the model was mtry = 3.
四．编辑 caretSBF
编辑 caretSBF 摘要函数
caretSBF$summary <- fiveStats
V。 SBF 部分
正在定义 sbfControl
sbfCtrl <- sbfControl(functions=caretSBF,
method="repeatedcv", number=10, repeats=1,
verbose=T, saveDetails = T)
SBF + 公制 = "Accuracy"
set.seed(1)
sbf_acc <- sbf(Num_Pred, BrC1$Class,
sbfControl = sbfCtrl,
trControl = trCtrl, method="rf", metric="Accuracy")
## sbf_acc
sbf_acc
# Selection By Filter
#
# Outer resampling method: Cross-Validated (10 fold, repeated 1 times)
#
# Resampling performance:
#
# ROC Sens Spec Accuracy Kappa ROCSD SensSD SpecSD AccuracySD KappaSD
# 0.9931 0.973 0.9833 0.9766 0.949 0.006272 0.0231 0.02913 0.01226 0.02646
#
# Using the training set, 9 variables were selected:
# Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, Epith.c.size...
#
# During resampling, the top 5 selected variables (out of a possible 9):
# Bare.nuclei (100%), Bl.cromatin (100%), Cell.shape (100%), Cell.size (100%), Cl.thickness (100%)
#
# On average, 9 variables were selected (min = 9, max = 9)
## Class of sbf_acc
class(sbf_acc)
# [1] "sbf"
## Names of elements of sbf_acc
names(sbf_acc)
# [1] "pred" "variables" "results" "fit" "optVariables"
# [6] "call" "control" "resample" "metrics" "times"
# [11] "resampledCM" "obsLevels" "dots"
## sbf_acc fit element*
sbf_acc$fit
# Random Forest
#
# 683 samples
# 9 predictor
# 2 classes: 'benign', 'malignant'
#
# No pre-processing
# Resampling: Cross-Validated (10 fold, repeated 1 times)
# Summary of sample sizes: 615, 614, 614, 615, 615, 615, ...
# Resampling results across tuning parameters:
#
# mtry ROC Sens Spec Accuracy Kappa
# 2 0.9933176 0.9706566 0.9833333 0.9751492 0.9460717
# 5 0.9920034 0.9662121 0.9791667 0.9707801 0.9363708
# 9 0.9914825 0.9684343 0.9708333 0.9693308 0.9327662
#
# Accuracy was used to select the optimal model using the largest value.
# The final value used for the model was mtry = 2.
## Elements of sbf_acc fit
names(sbf_acc$fit)
# [1] "method" "modelInfo" "modelType" "results" "pred"
# [6] "bestTune" "call" "dots" "metric" "control"
# [11] "finalModel" "preProcess" "trainingData" "resample" "resampledCM"
# [16] "perfNames" "maximize" "yLimits" "times" "levels"
## sbf_acc fit final Model
sbf_acc$fit$finalModel
# Call:
# randomForest(x = x, y = y, mtry = param$mtry)
# Type of random forest: classification
# Number of trees: 500
# No. of variables tried at each split: 2
#
# OOB estimate of error rate: 2.34%
# Confusion matrix:
# benign malignant class.error
# benign 431 13 0.02927928
# malignant 3 236 0.01255230
## sbf_acc metric
sbf_acc$fit$metric
# [1] "Accuracy"
## sbf_acc fit best Tune*
sbf_acc$fit$bestTune
# mtry
# 1 2
SBF + 公制 = "ROC"
set.seed(1)
sbf_roc <- sbf(Num_Pred, BrC1$Class,
sbfControl = sbfCtrl,
trControl = trCtrl, method="rf", metric="ROC")
## sbf_roc
sbf_roc
# Selection By Filter
#
# Outer resampling method: Cross-Validated (10 fold, repeated 1 times)
#
# Resampling performance:
#
# ROC Sens Spec Accuracy Kappa ROCSD SensSD SpecSD AccuracySD KappaSD
# 0.9931 0.973 0.9833 0.9766 0.949 0.006272 0.0231 0.02913 0.01226 0.02646
#
# Using the training set, 9 variables were selected:
# Cl.thickness, Cell.size, Cell.shape, Marg.adhesion, Epith.c.size...
#
# During resampling, the top 5 selected variables (out of a possible 9):
# Bare.nuclei (100%), Bl.cromatin (100%), Cell.shape (100%), Cell.size (100%), Cl.thickness (100%)
#
# On average, 9 variables were selected (min = 9, max = 9)
## Class of sbf_roc
class(sbf_roc)
# [1] "sbf"
## Names of elements of sbf_roc
names(sbf_roc)
# [1] "pred" "variables" "results" "fit" "optVariables"
# [6] "call" "control" "resample" "metrics" "times"
# [11] "resampledCM" "obsLevels" "dots"
## sbf_roc fit element*
sbf_roc$fit
# Random Forest
#
# 683 samples
# 9 predictor
# 2 classes: 'benign', 'malignant'
#
# No pre-processing
# Resampling: Cross-Validated (10 fold, repeated 1 times)
# Summary of sample sizes: 615, 614, 614, 615, 615, 615, ...
# Resampling results across tuning parameters:
#
# mtry ROC Sens Spec Accuracy Kappa
# 2 0.9933176 0.9706566 0.9833333 0.9751492 0.9460717
# 5 0.9920034 0.9662121 0.9791667 0.9707801 0.9363708
# 9 0.9914825 0.9684343 0.9708333 0.9693308 0.9327662
#
# ROC was used to select the optimal model using the largest value.
# The final value used for the model was mtry = 2.
## Elements of sbf_roc fit
names(sbf_roc$fit)
# [1] "method" "modelInfo" "modelType" "results" "pred"
# [6] "bestTune" "call" "dots" "metric" "control"
# [11] "finalModel" "preProcess" "trainingData" "resample" "resampledCM"
# [16] "perfNames" "maximize" "yLimits" "times" "levels"
## sbf_roc fit final Model
sbf_roc$fit$finalModel
# Call:
# randomForest(x = x, y = y, mtry = param$mtry)
# Type of random forest: classification
# Number of trees: 500
# No. of variables tried at each split: 2
#
# OOB estimate of error rate: 2.34%
# Confusion matrix:
# benign malignant class.error
# benign 431 13 0.02927928
# malignant 3 236 0.01255230
## sbf_roc metric
sbf_roc$fit$metric
# [1] "ROC"
## sbf_roc fit best Tune
sbf_roc$fit$bestTune
# mtry
# 1 2
sbf() 是否使用 metric 参数来优化模型？如果是，metric sbf() 默认使用什么？如果 sbf() 使用 metric 参数，那么如何将其设置为 ROC?
谢谢。
sbf 不使用度量来优化任何东西（不像 rfe）； sbf 所做的只是在调用模型之前执行特征选择步骤。当然，您定义了过滤器，但无法使用 sbf 调整过滤器，因此不需要指标来指导该步骤。
使用 sbf(x, y, metric = "ROC") 会将 metric = "ROC" 传递给您正在使用的任何建模函数（并且它设计为在使用 caretSBF 时与 train 一起使用。发生这种情况是因为sbf:
metric 参数
> names(formals(caret:::sbf.default))
[1] "x" "y" "sbfControl" "..."