如何计算随机森林模型的Brier得分？

dbf7pr2w  于 2023-06-03  发布在  其他
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我建立了一个随机森林模型，结果非常差，我试图评估模型的类成员概率，而不是它在分类中的表现。
数据由许多预测特征和目标特征（列）和样本（行）组成。目标特征为二进制（是/否）。所有预测特征都是数字。只有目标特征是分类的。
这是使用caret包的模型拟合：

ctrlCV = trainControl(method = 'cv', number = 10 , classProbs = TRUE , savePredictions = TRUE, summaryFunction = twoClassSummary )

rfFit <- train(y~., data = train_set,
               importance = TRUE,
               method = "rf",
               metric="ROC",
               trControl = ctrlCV,
               tuneLength = 5
)

我在这里看到了一个类似的主题，我从它那里取了这个代码，但它不起作用：

prediction = predict(rfFit, train_set , na.action="na.impute")
BrierScore(as.numeric(train_set$y) - 1, prediction$predicted[, 1L])

这就是它产生的错误：

Error in na.impute(list(Adipocytes = c(0.0148875691547336, 0.0148875691547336,  : 
  could not find function "na.impute"

如何计算这样一个模型的Brier分数？

EDIT -数据示例：
structure(list(response = c("NoResponse", "NoResponse", "NoResponse", 
"NoResponse", "NoResponse", "NoResponse", "NoResponse", "NoResponse", 
"NoResponse", "NoResponse", "NoResponse", "NoResponse", "NoResponse", 
"NoResponse", "NoResponse", "NoResponse", "NoResponse", "NoResponse", 
"NoResponse", "NoResponse", "NoResponse", "NoResponse", "NoResponse", 
"NoResponse", "NoResponse", "NoResponse", "NoResponse", "NoResponse", 
"NoResponse", "NoResponse", "NoResponse", "NoResponse", "NoResponse", 
"NoResponse", "NoResponse", "NoResponse", "NoResponse", "NoResponse", 
"NoResponse", "Response", "Response", "Response", "Response", 
"Response", "Response", "Response", "Response", "Response", "Response", 
"NoResponse"), Adipocytes = c(0.0148875691547336, 0.0148875691547336, 
0.0148875691547336, 0.0148875691547336, 0.0213214758191608, 0.0711663970487551, 
0.0148875691547336, 0.0148875691547336, 0.0148875691547336, 0.195678965015043, 
0.0148875691547336, 0.031597518280539, 0.0210749233513755, 0.0148875691547336, 
0.0148875691547336, 0.0148875691547336, 0.0386946286430843, 0.0148875691547336, 
0.457259819150423, 0.0148875691547336, 0.0148875691547336, 0.0148875691547336, 
0.0148875691547336, 0.023032072529065, 0.0373511495950606, 0.0148875691547336, 
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0.0148875691547336, 0.0148875691547336, 0.0503553785018031, 0.0148875691547336, 
0.0148875691547336, 0.023654425422698, 0.0148875691547336, 0.00301575533285113
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    0.0232397214310027, 0.0264444078129541, 0.0166411831489929, 
    0.0166411831489929, 0.0166411831489929, 0.0166411831489929, 
    0.0166411831489929, 0.0632255860984502, 0.0223371324350743, 
    0.0166411831489929, 0.0381409123497947, 0.0166411831489929, 
    0.0166411831489929, 0.0166411831489929, 0.0579165289705117, 
    0.0166411831489929, 0.0166411831489929, 0.0389383046357601, 
    0.0746421132626324, 0.0166411831489929, -0.0510045740152101
    ), Macrophages.M1 = c(0.0939027028913093, 0.00991806048158322, 
    0.0307250297361532, 0.111143332595117, 0.0292284276226276, 
    0.0381839764683231, 0.0103302886442727, 0.0398979819807463, 
    0.105491423751765, 0.150250611917613, 0.00272760279247674, 
    0.030352141019262, 0.019100544774407, 0.0481314865500234, 
    0.0204723732124283, 0.00838577053175601, 0.0212662830801156, 
    0.176020297845548, 0.00272760279247674, 0.00997760514093038, 
    0.0178614585003549, 0.00272760279247674, 0.0138362702729734, 
    0.0385921702605774, 0.154611609585058, 0.0355816943811817, 
    0.0429897179271763, 0.06341462292347, 0.0376788779822781, 
    0.0424460141081865, 0.118451043972679, 0.0104964633803846, 
    0.0616039925956695, 0.0189581728968219, 0.0131898549270135, 
    0.0227127506572289, 0.0501975621261345, 0.00272760279247674, 
    0.00877120965369497, 0.0422300236889794, 0.0431469259855728, 
    0.00272760279247674, 0.0141554863447003, 0.0166436263190985, 
    0.0298201923666392, 0.0481122432136814, 0.0842495692975132, 
    0.0435379661848463, 0.0304321030763242, 0.0858878607975676
    ), Macrophages.M2 = c(0.0950370387250134, 0.0341021814039373, 
    0.0129080389068132, 0.136032292676889, 0.0500615096525892, 
    0.0261538120522582, 0.0262035916343674, 0.0379314707513149, 
    0.0634308346430593, 0.229510962749818, 0.0129080389068132, 
    0.0397807872973065, 0.031996379798556, 0.0490437826861079, 
    0.0129080389068132, 0.0129080389068132, 0.0479132930965825, 
    0.179068336862642, 0.167470177529083, 0.0210840359218022, 
    0.0247881531036544, 0.0129080389068132, 0.0241373555223528, 
    0.0129080389068132, 0.220088283054851, 0.0363913474266373, 
    0.102746064255281, 0.0568930515962548, 0.0730618463614969, 
    0.0190480628578201, 0.140445976081207, 0.0414388510084821, 
    0.0182236555221719, 0.0181665441709514, 0.0129080389068132, 
    0.0269146240316719, 0.0129080389068132, 0.0129080389068132, 
    0.0129080389068132, 0.031739051141187, 0.0643613066122741, 
    0.0263740661987764, 0.0129080389068132, 0.0129080389068132, 
    0.0129080389068132, 0.0359960508390566, 0.0276881038962302, 
    0.0880892425295165, 0.0486983682712236, 0.0819337135938423
    ), Mast.cells = c(0.00262272709651714, 0.00918956479724631, 
    0.0237863628013975, 0.0120882313148399, 0.00262272709651714, 
    0.00262272709651714, 0.0132078489828078, 0.00262272709651714, 
    0.00925484950428775, 0.0148295850584796, 0.0216140469381388, 
    0.0201444625393507, 0.00262272709651714, 0.00262272709651714, 
    0.00262272709651714, 0.00262272709651714, 0.00262272709651714, 
    0.0177982493490349, 0.0399020678006343, 0.00262272709651714, 
    0.0127350032738263, 0.010122250042933, 0.00262272709651714, 
    0.00262272709651714, 0.0560153757178254, 0.00262272709651714, 
    0.00262272709651714, 0.00262272709651714, 0.00262272709651714, 
    0.00262272709651714, 0.0100042201637379, 0.00262272709651714, 
    0.00262272709651714, 0.00262272709651714, 0.00262272709651714, 
    0.00262272709651714, 0.00262272709651714, 0.00262272709651714, 
    0.00262272709651714, 0.00262272709651714, 0.00262272709651714, 
    0.00966452021833436, 0.00262272709651714, 0.0162426439532491, 
    0.00262272709651714, 0.00262272709651714, 0.00882907219428391, 
    0.00262272709651714, 0.00936334626318404, 0.0325805463741492
    ), Melanocytes = c(0.197208530782834, 0.117972703910268, 
    -0.0602878219630949, 0.0094514247329539, 0.0720302427467801, 
    0.0535616550191451, 0.147656564458396, 0.00506865434571719, 
    0.0506729519536773, -0.0602878219630949, 0.171976680921397, 
    0.0391064859618761, 0.138335210852028, 0.0204085278110901, 
    0.166543177510308, 0.0391339248224207, -0.00585060388562906, 
    0.0907762414421459, -0.0602878219630949, 0.0942830836322463, 
    0.0722107746330366, 0.0489720312042079, 0.0101547247380102, 
    -0.0323689704172642, -0.0602878219630949, 0.0302364995883101, 
    -0.0164214064240953, 0.119210025484646, 0.125847753429396, 
    -0.0179013729637403, 0.0883487098541815, 0.111938605148703, 
    0.14062692562277, 0.126009766715894, 0.0090037910293348, 
    0.0714671785039881, -0.0142319167468624, 0.0692781943357738, 
    0.099402208705842, 0.04947201823059, 0.130279017998432, 0.136333775892651, 
    0.0607280725769218, 0.00232142515516216, -0.0602878219630949, 
    0.122589465900189, 0.0462738574752271, 0.131398436138857, 
    0.124808922845939, 0.0286661474012692), naive.B.cells = c(-0.00189298072743305, 
    -0.00189298072743305, 0.131760794411808, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    0.0576111720143354, -0.00189298072743305, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, 0.0356853961207387, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    -0.00189298072743305, -0.00189298072743305, -0.00189298072743305, 
    0.0789824945659587)), row.names = c("Pt1", "Pt10", "Pt101", 
"Pt103", "Pt106", "Pt11", "Pt17", "Pt18", "Pt2", "Pt24", "Pt26", 
"Pt27", "Pt28", "Pt29", "Pt3", "Pt30", "Pt31", "Pt34", "Pt36", 
"Pt37", "Pt38", "Pt39", "Pt4", "Pt44", "Pt46", "Pt47", "Pt48", 
"Pt49", "Pt5", "Pt52", "Pt59", "Pt62", "Pt65", "Pt66", "Pt67", 
"Pt72", "Pt77", "Pt78", "Pt79", "Pt8", "Pt82", "Pt84", "Pt85", 
"Pt89", "Pt9", "Pt90", "Pt92", "Pt94", "Pt98", "EA595454"), class = "data.frame")
r

2条答案

按热度按时间
zd287kbt

zd287kbt1#

你的代码将工作，如果你做2的变化
1.你必须计算预测的概率和
1.将观测值转换为0和1，如

library(caret)
library(DescTools)

prediction = predict(rfFit, train_set, type = "prob")

BrierScore(prediction[,1], ifelse(train_set$response == "Response", 1, 0))
#> [1] 0.86336
赞(0）分享  回复(0）举报  2023-06-03
gopyfrb3

gopyfrb32#

如果您使用Ranger随机森林软件包，概率= TRUE，则会为您计算Brier分数。只需训练模型，然后打印它。

print(rfFit)

...
OOB prediction error (Brier s.):  0.xxxxxxx
赞(0）分享  回复(0）举报  2023-06-03
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