How to calculate the Brier score for a random forest model?-CodePudding

I built a random forest model that yields very poor results and I'm trying to evaluate the model's probabilities of class membership instead of how good it is in classifications.

The data is made up of many predictive features and a target feature (the columns) and the samples (rows). The target feature is binary ( yes / no ). All predictive features are numeric. Only the target feature is catigorical.

This is the model fitting, using the caret package:

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
)

There is a similar subject I saw here, I took this code from it but it didn't work:

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

This is the error it yields:

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

How do I calculate the Brier score for such a model ?

EDIT - A sample of the data:

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, 
0.157860885224959, 0.395127785573274, 0.0148875691547336, 0.117206058831837, 
0.0201265559280696, 0.0148875691547336, 0.0148875691547336, 0.0148875691547336, 
0.0148875691547336, 0.0148875691547336, 0.0148875691547336, 0.0148875691547336, 
0.0148875691547336, 0.0307193488214309, 0.0463334794186272, 0.126003283111471, 
0.0148875691547336, 0.0148875691547336, 0.0503553785018031, 0.0148875691547336, 
0.0148875691547336, 0.023654425422698, 0.0148875691547336, 0.00301575533285113
), B.cells = c(0.0800057551371177, 0.0376859010636605, 1.23476138825399, 
0.169686091992878, 0.0318719603730758, 0.031752160845938, 0.0120156131970648, 
0.618090954863207, 0.0573855654207301, 0.201270076200368, 0.0348008563091038, 
0.02697257318329, 0.083033553134568, 0.0587037531654018, 0.0120156131970648, 
0.0400051010042196, 0.100754967768784, 0.0924894252873105, 0.043296797638842, 
0.0120156131970648, 0.018436681190321, 0.0120156131970648, 0.0221023538252602, 
0.0371660161819204, 0.12021632580784, 0.0270346525210825, 0.0350567231064043, 
0.135118624367071, 0.0645035015441352, 0.0120156131970648, 0.0593102756741702, 
0.0370267152338414, 0.0599853534350651, 0.0120156131970648, 0.0494246572280471, 
0.0232776770252547, 0.584766970357701, 0.0120156131970648, 0.0448607293071243, 
0.0359477973801615, 0.0481903125819609, 0.0120156131970648, 0.0120156131970648, 
0.0120156131970648, 0.0120156131970648, 0.0441403932340056, 0.062472410164602, 
0.056576445991063, 0.0347151937202121, 0.428724007963313), CD4..memory.T.cells = c(0.053222426779995, 
0.00498569417349055, 0.285609459967325, 0.00498569417349055, 
0.0133818188350683, 0.00498569417349055, 0.00498569417349055, 
0.079643550328675, 0.018577779597057, 0.0591669720162329, 0.00498569417349055, 
0.0346796513361763, 0.00498569417349055, 0.00498569417349055, 
0.00498569417349055, 0.00498569417349055, 0.0128068287182537, 
0.0387932856534556, 0.0106166902578661, 0.00498569417349055, 
0.00498569417349055, 0.00498569417349055, 0.00498569417349055, 
0.0221443888308076, 0.0384697960774856, 0.00498569417349055, 
0.00498569417349055, 0.0353051264006193, 0.0150418989575268, 
0.00498569417349055, 0.00498569417349055, 0.00498569417349055, 
0.00498569417349055, 0.00498569417349055, 0.0104113831098345, 
0.00498569417349055, 0.115449562173739, 0.00498569417349055, 
0.00498569417349055, 0.00498569417349055, 0.00498569417349055, 
0.00498569417349055, 0.00498569417349055, 0.00498569417349055, 
0.00498569417349055, 0.0225942479194889, 0.00498569417349055, 
0.0359483855877116, 0.00498569417349055, 0.101333999071329), 
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    0.00836792326030943, 0.00836792326030943, 0.0169971803647614, 
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    0.216327052965482, 0.0681402041738468, 0.108890303649405, 
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    0.0139475994797827, 0.22914061844728, 0.198185709502971, 
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    0.0350849726004794, -0.0150080105365044, 0.0531566439902232, 
    -0.00449122205516312, 0.229384982409473, -0.0244639754370864, 
    -0.0244639754370864, 0.0545693572660695, -0.00331283339707623, 
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    -0.0167953556780208, 0.130572694535018, 0.0967256087562913, 
    0.143433987544022, -0.0244639754370864, -0.0224591675274701
    ), CD8..T.cells = c(0.0494421812434541, 0.0153720771909336, 
    0.328623899153089, 0.047123341544761, 0.0399043585541585, 
    0.0153720771909336, 0.0153720771909336, 0.127964069489891, 
    0.0275505013045806, 0.0377127445480443, 0.0278609203031339, 
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    0.0153720771909336, 0.0610798919413866, 0.0153720771909336, 
    0.0153720771909336, 0.0153720771909336, 0.0257557521848714, 
    0.0153720771909336, 0.209423182002428, 0.0153720771909336, 
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    0.0153720771909336, 0.0153720771909336, 0.102816726458688, 
    0.030999206233028, 0.0153720771909336, 0.204491328877077), 
    CD8..Tcm = c(0.182655282069243, 0.0117462052409156, 0.495215120490835, 
    0.227880294685582, 0.0698694847307345, 0.0238038922607861, 
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    0.156670399719073, 0.0196128414307518, 0.0117462052409156, 
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    0.0117462052409156, 0.113544492227536, 0.355698724201404, 
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    0.0117462052409156, 0.26678368565526), Class.switched.memory.B.cells = c(0.0154278203400193, 
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    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")

CodePudding user response：

Your code will work if you make 2 changes

You have to calculate the predicted probability and
Convert the observations to 0 and 1 like

library(caret)
library(DescTools)

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

BrierScore(prediction[,1], ifelse(train_set$response == "Response", 1, 0))
#> [1] 0.86336