R Random Forest Tutorial s primjerom
Evelyn Clarke
ล to je Random Forest u R?
Nasumiฤne ลกume temelje se na jednostavnoj ideji: 'mudrosti gomile'. Zbir rezultata viลกestrukih prediktora daje bolje predviฤanje od najboljeg pojedinaฤnog prediktora. Skupina prediktora naziva se an ansambl. Dakle, ova tehnika se zove Uฤenje ansambla.
U prethodnom vodiฤu nauฤili ste kako koristiti Stabla odluฤivanja napraviti binarno predviฤanje. Kako bismo poboljลกali svoju tehniku, moลพemo trenirati grupu Klasifikatori stabla odluฤivanja, svaki na razliฤitom sluฤajnom podskupu skupa vlakova. Da bismo napravili predviฤanje, samo dobivamo predviฤanja svih pojedinaฤnih stabala, zatim predviฤamo klasu koja dobiva najviลกe glasova. Ova tehnika se zove Sluฤajna ลกuma.
Korak 1) Uvezite podatke
Kako biste bili sigurni da imate isti skup podataka kao u vodiฤu za stabla odluฤivanja, test vlaka i ispitni skup pohranjeni su na internetu. Moลพete ih uvesti bez ikakvih promjena.
library(dplyr)
data_train <- read.csv("https://raw.githubusercontent.com/guru99-edu/R-Programming/master/train.csv")
glimpse(data_train)
data_test <- read.csv("https://raw.githubusercontent.com/guru99-edu/R-Programming/master/test.csv")
glimpse(data_test)
Korak 2) Uvjeลพbajte model
Jedan od naฤina da se ocijeni izvedba modela je da se uvjeลพba na viลกe razliฤitih manjih skupova podataka i da se oni ocijene na drugom manjem skupu za testiranje. Ovo se zove F-fold unakrsna provjera valjanosti znaฤajka. R ima funkciju za nasumiฤno dijeljenje skupova podataka gotovo iste veliฤine. Na primjer, ako je k=9, model se procjenjuje u devet mapa i testira na preostalom testnom skupu. Ovaj se postupak ponavlja dok se ne procijene svi podskupovi. Ova tehnika se naลกiroko koristi za odabir modela, posebno kada model ima parametre za podeลกavanje.
Sada kada imamo naฤin da ocijenimo naลก model, moramo smisliti kako odabrati parametre koji najbolje generaliziraju podatke.
Nasumiฤna ลกuma odabire nasumiฤni podskup znaฤajki i gradi mnoga stabla odluฤivanja. Model izraฤunava prosjek svih predviฤanja stabala odluka.
Sluฤajna ลกuma ima neke parametre koji se mogu promijeniti kako bi se poboljลกala generalizacija predviฤanja. Koristit ฤete funkciju RandomForest() za obuku modela.
Sintaksa za Randon Forest je
RandomForest(formula, ntree=n, mtry=FALSE, maxnodes = NULL) Arguments: - Formula: Formula of the fitted model - ntree: number of trees in the forest - mtry: Number of candidates draw to feed the algorithm. By default, it is the square of the number of columns. - maxnodes: Set the maximum amount of terminal nodes in the forest - importance=TRUE: Whether independent variables importance in the random forest be assessed
biljeลกke: Nasumiฤna ลกuma moลพe se trenirati na viลกe parametara. Moลพete se obratiti na vinjeta kako biste vidjeli razliฤite parametre.
Ugaฤanje modela vrlo je naporan posao. Postoji mnogo moguฤih kombinacija izmeฤu parametara. Ne morate nuลพno imati vremena da ih sve isprobate. Dobra alternativa je pustiti stroj da pronaฤe najbolju kombinaciju za vas. Dostupne su dvije metode:
- Nasumiฤno pretraลพivanje
- Mreลพno pretraลพivanje
Definirat ฤemo obje metode, ali ฤemo tijekom poduke uvjeลพbati model pomoฤu pretraลพivanja mreลพe
Definicija pretraลพivanja mreลพe
Metoda pretraลพivanja mreลพe je jednostavna, model ฤe se procijeniti preko svih kombinacija koje proslijedite u funkciji, koristeฤi unakrsnu provjeru.
Na primjer, ลพelite isprobati model s 10, 20, 30 stabala i svako ฤe se stablo testirati tijekom broja mtry jednakih 1, 2, 3, 4, 5. Zatim ฤe stroj testirati 15 razliฤitih modela:
.mtry ntrees 1 1 10 2 2 10 3 3 10 4 4 10 5 5 10 6 1 20 7 2 20 8 3 20 9 4 20 10 5 20 11 1 30 12 2 30 13 3 30 14 4 30 15 5 30
Algoritam ฤe procijeniti:
RandomForest(formula, ntree=10, mtry=1) RandomForest(formula, ntree=10, mtry=2) RandomForest(formula, ntree=10, mtry=3) RandomForest(formula, ntree=20, mtry=2) ...
Svaki put nasumiฤna ลกuma eksperimentira s unakrsnom provjerom. Jedan nedostatak pretraลพivanja mreลพe je broj pokusa. Vrlo lako moลพe postati eksplozivan kada je broj kombinacija velik. Da biste rijeลกili ovaj problem, moลพete upotrijebiti nasumiฤno pretraลพivanje
Definicija nasumiฤnog pretraลพivanja
Velika razlika izmeฤu nasumiฤnog pretraลพivanja i pretraลพivanja mreลพe je ลกto nasumiฤno pretraลพivanje neฤe procijeniti sve kombinacije hiperparametara u prostoru pretraลพivanja. Umjesto toga, nasumiฤno ฤe odabrati kombinaciju pri svakoj iteraciji. Prednost je niลพi troลกak raฤunanja.
Postavite kontrolni parametar
Za izradu i procjenu modela postupit ฤete na sljedeฤi naฤin:
- Procijenite model sa zadanom postavkom
- Pronaฤite najbolji broj mtry
- Pronaฤite najbolji broj maksimalnih ฤvorova
- Pronaฤite najbolji broj n-stabala
- Ocijenite model na testnom skupu podataka
Prije nego poฤnete s istraลพivanjem parametara, trebate instalirati dvije biblioteke.
- caret: R biblioteka strojnog uฤenja. Ako imate instalirati R s r-bitnim. Veฤ je u knjiลพnici
- anakonda: conda install -cr r-caret
- e1071: R biblioteka strojnog uฤenja.
- anakonda: conda install -cr r-e1071
Moลพete ih uvesti zajedno s RandomForestom
library(randomForest) library(caret) library(e1071)
Tvorniฤke postavke
Unakrsnu provjeru K-preklopa kontrolira funkcija trainControl().
trainControl(method = "cv", number = n, search ="grid") arguments - method = "cv": The method used to resample the dataset. - number = n: Number of folders to create - search = "grid": Use the search grid method. For randomized method, use "grid" Note: You can refer to the vignette to see the other arguments of the function.
Moลพete pokuลกati pokrenuti model sa zadanim parametrima i vidjeti ocjenu toฤnosti.
biljeลกke: Koristit ฤete iste kontrole tijekom cijelog poduฤavanja.
# Define the control
trControl <- trainControl(method = "cv",
number = 10,
search = "grid")
Za procjenu svog modela koristit ฤete biblioteku karata. Knjiลพnica ima jednu funkciju koja se zove train() za procjenu gotovo svih stroj za uฤenje algoritam. Recimo drugaฤije, ovu funkciju moลพete koristiti za treniranje drugih algoritama.
Osnovna sintaksa je:
train(formula, df, method = "rf", metric= "Accuracy", trControl = trainControl(), tuneGrid = NULL) argument - `formula`: Define the formula of the algorithm - `method`: Define which model to train. Note, at the end of the tutorial, there is a list of all the models that can be trained - `metric` = "Accuracy": Define how to select the optimal model - `trControl = trainControl()`: Define the control parameters - `tuneGrid = NULL`: Return a data frame with all the possible combination
Pokuลกajmo izgraditi model sa zadanim vrijednostima.
set.seed(1234)
# Run the model
rf_default <- train(survived~.,
data = data_train,
method = "rf",
metric = "Accuracy",
trControl = trControl)
# Print the results
print(rf_default)
Objaลกnjenje koda
- trainControl(method=โcvโ, broj=10, search=โgridโ): Procijenite model s pretraลพivanjem mreลพe od 10 mapa
- treniraj(โฆ): treniraj sluฤajni model ลกume. Najbolji model bira se s mjerom toฤnosti.
Izlaz:
## Random Forest ## ## 836 samples ## 7 predictor ## 2 classes: 'No', 'Yes' ## ## No pre-processing ## Resampling: Cross-Validated (10 fold) ## Summary of sample sizes: 753, 752, 753, 752, 752, 752, ... ## Resampling results across tuning parameters: ## ## mtry Accuracy Kappa ## 2 0.7919248 0.5536486 ## 6 0.7811245 0.5391611 ## 10 0.7572002 0.4939620 ## ## Accuracy was used to select the optimal model using the largest value. ## The final value used for the model was mtry = 2.
Algoritam koristi 500 stabala i testirao je tri razliฤite vrijednosti mtry: 2, 6, 10.
Konaฤna vrijednost koriลกtena za model bila je mtry = 2 s toฤnoลกฤu od 0.78. Pokuลกajmo postiฤi veฤi rezultat.
Korak 2) Potraลพite najbolju mtry
Model moลพete testirati s vrijednostima mtry od 1 do 10
set.seed(1234)
tuneGrid <- expand.grid(.mtry = c(1: 10))
rf_mtry <- train(survived~.,
data = data_train,
method = "rf",
metric = "Accuracy",
tuneGrid = tuneGrid,
trControl = trControl,
importance = TRUE,
nodesize = 14,
ntree = 300)
print(rf_mtry)
Objaลกnjenje koda
- tuneGrid <- expand.grid(.mtry=c(3:10)): Konstruirajte vektor s vrijednoลกฤu od 3:10
Konaฤna vrijednost koriลกtena za model bila je mtry = 4.
Izlaz:
## Random Forest ## ## 836 samples ## 7 predictor ## 2 classes: 'No', 'Yes' ## ## No pre-processing ## Resampling: Cross-Validated (10 fold) ## Summary of sample sizes: 753, 752, 753, 752, 752, 752, ... ## Resampling results across tuning parameters: ## ## mtry Accuracy Kappa ## 1 0.7572576 0.4647368 ## 2 0.7979346 0.5662364 ## 3 0.8075158 0.5884815 ## 4 0.8110729 0.5970664 ## 5 0.8074727 0.5900030 ## 6 0.8099111 0.5949342 ## 7 0.8050918 0.5866415 ## 8 0.8050918 0.5855399 ## 9 0.8050631 0.5855035 ## 10 0.7978916 0.5707336 ## ## Accuracy was used to select the optimal model using the largest value. ## The final value used for the model was mtry = 4.
Najbolja vrijednost mtry pohranjena je u:
rf_mtry$bestTune$mtry
Moลพete ga pohraniti i koristiti kada trebate podesiti ostale parametre.
max(rf_mtry$results$Accuracy)
Izlaz:
## [1] 0.8110729
best_mtry <- rf_mtry$bestTune$mtry best_mtry
Izlaz:
## [1] 4
Korak 3) Potraลพite najbolje maksimalne ฤvorove
Morate stvoriti petlju za procjenu razliฤitih vrijednosti maksimalnih ฤvorova. U sljedeฤem kodu ฤete:
- Stvorite popis
- Kreirajte varijablu s najboljom vrijednoลกฤu parametra mtry; Obavezno
- Napravite petlju
- Pohrani trenutnu vrijednost maxnode
- Saลพmite rezultate
store_maxnode <- list()
tuneGrid <- expand.grid(.mtry = best_mtry)
for (maxnodes in c(5: 15)) {
set.seed(1234)
rf_maxnode <- train(survived~.,
data = data_train,
method = "rf",
metric = "Accuracy",
tuneGrid = tuneGrid,
trControl = trControl,
importance = TRUE,
nodesize = 14,
maxnodes = maxnodes,
ntree = 300)
current_iteration <- toString(maxnodes)
store_maxnode[[current_iteration]] <- rf_maxnode
}
results_mtry <- resamples(store_maxnode)
summary(results_mtry)
Objaลกnjenje koda:
- store_maxnode <- list(): Rezultati modela bit ฤe pohranjeni na ovoj listi
- expand.grid(.mtry=best_mtry): Koristite najbolju vrijednost mtry
- for (maxnodes in c(15:25)) { โฆ }: Izraฤunajte model s vrijednostima maxnodes poฤevลกi od 15 do 25.
- maxnodes=maxnodes: Za svaku iteraciju, maxnodes je jednak trenutnoj vrijednosti maxnodes. tj. 15, 16, 17, โฆ
- kljuฤ <- toString(maxnodes): Pohrani kao string varijablu vrijednost maxnode.
- store_maxnode[[kljuฤ]] <- rf_maxnode: Spremi rezultat modela na popis.
- resamples(store_maxnode): Rasporedi rezultate modela
- summary(results_mtry): Ispis saลพetka svih kombinacija.
Izlaz:
## ## Call: ## summary.resamples(object = results_mtry) ## ## Models: 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 ## Number of resamples: 10 ## ## Accuracy ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 5 0.6785714 0.7529762 0.7903758 0.7799771 0.8168388 0.8433735 0 ## 6 0.6904762 0.7648810 0.7784710 0.7811962 0.8125000 0.8313253 0 ## 7 0.6904762 0.7619048 0.7738095 0.7788009 0.8102410 0.8333333 0 ## 8 0.6904762 0.7627295 0.7844234 0.7847820 0.8184524 0.8433735 0 ## 9 0.7261905 0.7747418 0.8083764 0.7955250 0.8258749 0.8333333 0 ## 10 0.6904762 0.7837780 0.7904475 0.7895869 0.8214286 0.8433735 0 ## 11 0.7023810 0.7791523 0.8024240 0.7943775 0.8184524 0.8433735 0 ## 12 0.7380952 0.7910929 0.8144005 0.8051205 0.8288511 0.8452381 0 ## 13 0.7142857 0.8005952 0.8192771 0.8075158 0.8403614 0.8452381 0 ## 14 0.7380952 0.7941050 0.8203528 0.8098967 0.8403614 0.8452381 0 ## 15 0.7142857 0.8000215 0.8203528 0.8075301 0.8378873 0.8554217 0 ## ## Kappa ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 5 0.3297872 0.4640436 0.5459706 0.5270773 0.6068751 0.6717371 0 ## 6 0.3576471 0.4981484 0.5248805 0.5366310 0.6031287 0.6480921 0 ## 7 0.3576471 0.4927448 0.5192771 0.5297159 0.5996437 0.6508314 0 ## 8 0.3576471 0.4848320 0.5408159 0.5427127 0.6200253 0.6717371 0 ## 9 0.4236277 0.5074421 0.5859472 0.5601687 0.6228626 0.6480921 0 ## 10 0.3576471 0.5255698 0.5527057 0.5497490 0.6204819 0.6717371 0 ## 11 0.3794326 0.5235007 0.5783191 0.5600467 0.6126720 0.6717371 0 ## 12 0.4460432 0.5480930 0.5999072 0.5808134 0.6296780 0.6717371 0 ## 13 0.4014252 0.5725752 0.6087279 0.5875305 0.6576219 0.6678832 0 ## 14 0.4460432 0.5585005 0.6117973 0.5911995 0.6590982 0.6717371 0 ## 15 0.4014252 0.5689401 0.6117973 0.5867010 0.6507194 0.6955990 0
Posljednja vrijednost maxnode ima najveฤu toฤnost. Moลพete pokuลกati s viลกim vrijednostima da vidite moลพete li dobiti veฤi rezultat.
store_maxnode <- list()
tuneGrid <- expand.grid(.mtry = best_mtry)
for (maxnodes in c(20: 30)) {
set.seed(1234)
rf_maxnode <- train(survived~.,
data = data_train,
method = "rf",
metric = "Accuracy",
tuneGrid = tuneGrid,
trControl = trControl,
importance = TRUE,
nodesize = 14,
maxnodes = maxnodes,
ntree = 300)
key <- toString(maxnodes)
store_maxnode[[key]] <- rf_maxnode
}
results_node <- resamples(store_maxnode)
summary(results_node)
Izlaz:
## ## Call: ## summary.resamples(object = results_node) ## ## Models: 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 ## Number of resamples: 10 ## ## Accuracy ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 20 0.7142857 0.7821644 0.8144005 0.8075301 0.8447719 0.8571429 0 ## 21 0.7142857 0.8000215 0.8144005 0.8075014 0.8403614 0.8571429 0 ## 22 0.7023810 0.7941050 0.8263769 0.8099254 0.8328313 0.8690476 0 ## 23 0.7023810 0.7941050 0.8263769 0.8111302 0.8447719 0.8571429 0 ## 24 0.7142857 0.7946429 0.8313253 0.8135112 0.8417599 0.8690476 0 ## 25 0.7142857 0.7916667 0.8313253 0.8099398 0.8408635 0.8690476 0 ## 26 0.7142857 0.7941050 0.8203528 0.8123207 0.8528758 0.8571429 0 ## 27 0.7023810 0.8060456 0.8313253 0.8135112 0.8333333 0.8690476 0 ## 28 0.7261905 0.7941050 0.8203528 0.8111015 0.8328313 0.8690476 0 ## 29 0.7142857 0.7910929 0.8313253 0.8087063 0.8333333 0.8571429 0 ## 30 0.6785714 0.7910929 0.8263769 0.8063253 0.8403614 0.8690476 0 ## ## Kappa ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 20 0.3956835 0.5316120 0.5961830 0.5854366 0.6661120 0.6955990 0 ## 21 0.3956835 0.5699332 0.5960343 0.5853247 0.6590982 0.6919315 0 ## 22 0.3735084 0.5560661 0.6221836 0.5914492 0.6422128 0.7189781 0 ## 23 0.3735084 0.5594228 0.6228827 0.5939786 0.6657372 0.6955990 0 ## 24 0.3956835 0.5600352 0.6337821 0.5992188 0.6604703 0.7189781 0 ## 25 0.3956835 0.5530760 0.6354875 0.5912239 0.6554912 0.7189781 0 ## 26 0.3956835 0.5589331 0.6136074 0.5969142 0.6822128 0.6955990 0 ## 27 0.3735084 0.5852459 0.6368425 0.5998148 0.6426088 0.7189781 0 ## 28 0.4290780 0.5589331 0.6154905 0.5946859 0.6356141 0.7189781 0 ## 29 0.4070588 0.5534173 0.6337821 0.5901173 0.6423101 0.6919315 0 ## 30 0.3297872 0.5534173 0.6202632 0.5843432 0.6590982 0.7189781 0
Najveฤa ocjena toฤnosti dobiva se s vrijednoลกฤu maxnode jednakom 22.
Korak 4) Potraลพite najbolja stabla
Sada kada imate najbolju vrijednost mtry i maxnode, moลพete podesiti broj stabala. Metoda je potpuno ista kao i maxnode.
store_maxtrees <- list()
for (ntree in c(250, 300, 350, 400, 450, 500, 550, 600, 800, 1000, 2000)) {
set.seed(5678)
rf_maxtrees <- train(survived~.,
data = data_train,
method = "rf",
metric = "Accuracy",
tuneGrid = tuneGrid,
trControl = trControl,
importance = TRUE,
nodesize = 14,
maxnodes = 24,
ntree = ntree)
key <- toString(ntree)
store_maxtrees[[key]] <- rf_maxtrees
}
results_tree <- resamples(store_maxtrees)
summary(results_tree)
Izlaz:
## ## Call: ## summary.resamples(object = results_tree) ## ## Models: 250, 300, 350, 400, 450, 500, 550, 600, 800, 1000, 2000 ## Number of resamples: 10 ## ## Accuracy ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 250 0.7380952 0.7976190 0.8083764 0.8087010 0.8292683 0.8674699 0 ## 300 0.7500000 0.7886905 0.8024240 0.8027199 0.8203397 0.8452381 0 ## 350 0.7500000 0.7886905 0.8024240 0.8027056 0.8277623 0.8452381 0 ## 400 0.7500000 0.7886905 0.8083764 0.8051009 0.8292683 0.8452381 0 ## 450 0.7500000 0.7886905 0.8024240 0.8039104 0.8292683 0.8452381 0 ## 500 0.7619048 0.7886905 0.8024240 0.8062914 0.8292683 0.8571429 0 ## 550 0.7619048 0.7886905 0.8083764 0.8099062 0.8323171 0.8571429 0 ## 600 0.7619048 0.7886905 0.8083764 0.8099205 0.8323171 0.8674699 0 ## 800 0.7619048 0.7976190 0.8083764 0.8110820 0.8292683 0.8674699 0 ## 1000 0.7619048 0.7976190 0.8121510 0.8086723 0.8303571 0.8452381 0 ## 2000 0.7619048 0.7886905 0.8121510 0.8086723 0.8333333 0.8452381 0 ## ## Kappa ## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's ## 250 0.4061697 0.5667400 0.5836013 0.5856103 0.6335363 0.7196807 0 ## 300 0.4302326 0.5449376 0.5780349 0.5723307 0.6130767 0.6710843 0 ## 350 0.4302326 0.5449376 0.5780349 0.5723185 0.6291592 0.6710843 0 ## 400 0.4302326 0.5482030 0.5836013 0.5774782 0.6335363 0.6710843 0 ## 450 0.4302326 0.5449376 0.5780349 0.5750587 0.6335363 0.6710843 0 ## 500 0.4601542 0.5449376 0.5780349 0.5804340 0.6335363 0.6949153 0 ## 550 0.4601542 0.5482030 0.5857118 0.5884507 0.6396872 0.6949153 0 ## 600 0.4601542 0.5482030 0.5857118 0.5884374 0.6396872 0.7196807 0 ## 800 0.4601542 0.5667400 0.5836013 0.5910088 0.6335363 0.7196807 0 ## 1000 0.4601542 0.5667400 0.5961590 0.5857446 0.6343666 0.6678832 0 ## 2000 0.4601542 0.5482030 0.5961590 0.5862151 0.6440678 0.6656337 0
Imate svoj konaฤni model. Sluฤajnu ลกumu moลพete trenirati sa sljedeฤim parametrima:
- ntree =800: 800 stabala ฤe biti obuฤeno
- mtry=4: 4 znaฤajke su odabrane za svaku iteraciju
- maxnodes = 24: Maksimalno 24 ฤvora u terminalnim ฤvorovima (liลกฤe)
fit_rf <- train(survived~.,
data_train,
method = "rf",
metric = "Accuracy",
tuneGrid = tuneGrid,
trControl = trControl,
importance = TRUE,
nodesize = 14,
ntree = 800,
maxnodes = 24)
Korak 5) Procijenite model
Biblioteฤna oznaka ima funkciju predviฤanja.
predict(model, newdata= df) argument - `model`: Define the model evaluated before. - `newdata`: Define the dataset to make prediction
prediction <-predict(fit_rf, data_test)
Moลพete koristiti predviฤanje za izraฤunavanje matrice zabune i vidjeti ocjenu toฤnosti
confusionMatrix(prediction, data_test$survived)
Izlaz:
## Confusion Matrix and Statistics ## ## Reference ## Prediction No Yes ## No 110 32 ## Yes 11 56 ## ## Accuracy : 0.7943 ## 95% CI : (0.733, 0.8469) ## No Information Rate : 0.5789 ## P-Value [Acc > NIR] : 3.959e-11 ## ## Kappa : 0.5638 ## Mcnemar's Test P-Value : 0.002289 ## ## Sensitivity : 0.9091 ## Specificity : 0.6364 ## Pos Pred Value : 0.7746 ## Neg Pred Value : 0.8358 ## Prevalence : 0.5789 ## Detection Rate : 0.5263 ## Detection Prevalence : 0.6794 ## Balanced Accuracy : 0.7727 ## ## 'Positive' Class : No ##
Imate toฤnost od 0.7943 posto, ลกto je viลกe od zadane vrijednosti
Korak 6) Vizualizirajte rezultat
Na kraju, moลพete pogledati vaลพnost znaฤajke pomoฤu funkcije varImp(). ฤini se da su najvaลพnija obiljeลพja spol i dob. To nije iznenaฤujuฤe jer ฤe se vaลพne znaฤajke vjerojatno pojaviti bliลพe korijenu stabla, dok ฤe se manje vaลพne znaฤajke ฤesto pojaviti blizu liลกฤa.
varImpPlot(fit_rf)
Izlaz:
varImp(fit_rf) ## rf variable importance ## ## Importance ## sexmale 100.000 ## age 28.014 ## pclassMiddle 27.016 ## fare 21.557 ## pclassUpper 16.324 ## sibsp 11.246 ## parch 5.522 ## embarkedC 4.908 ## embarkedQ 1.420 ## embarkedS 0.000
Rezime
Moลพemo saลพeti kako trenirati i ocijeniti sluฤajnu ลกumu pomoฤu tablice u nastavku:
| Knjiลพnica | Cilj | funkcija | Parametar |
|---|---|---|---|
| randomForest | Stvorite sluฤajnu ลกumu | RandomForest() | formula, ntree=n, mtry=FALSE, maxnodes = NULL |
| znak za umetanje | Stvorite unakrsnu provjeru mape K | trainControl() | metoda = โcvโ, broj = n, pretraga =โmreลพaโ |
| znak za umetanje | Trenirajte nasumiฤne ลกume | vlak() | formula, df, metoda = โrfโ, metrika = โToฤnostโ, trControl = trainControl(), tuneGrid = NULL |
| znak za umetanje | Predvidjeti izvan uzorka | predvidjeti | model, novi podaci= df |
| znak za umetanje | Matrica zabune i statistika | Matrica zbunjenosti() | model, y test |
| znak za umetanje | promjenljiva vaลพnost | cvarImp() | model |
Dodatak
Popis modela koriลกtenih u umetanju
names>(getModelInfo())
Izlaz:
## [1] "ada" "AdaBag" "AdaBoost.M1" ## [4] "adaboost" "amdai" "ANFIS" ## [7] "avNNet" "awnb" "awtan" ## [10] "bag" "bagEarth" "bagEarthGCV" ## [13] "bagFDA" "bagFDAGCV" "bam" ## [16] "bartMachine" "bayesglm" "binda" ## [19] "blackboost" "blasso" "blassoAveraged" ## [22] "bridge" "brnn" "BstLm" ## [25] "bstSm" "bstTree" "C5.0" ## [28] "C5.0Cost" "C5.0Rules" "C5.0Tree" ## [31] "cforest" "chaid" "CSimca" ## [34] "ctree" "ctree2" "cubist" ## [37] "dda" "deepboost" "DENFIS" ## [40] "dnn" "dwdLinear" "dwdPoly" ## [43] "dwdRadial" "earth" "elm" ## [46] "enet" "evtree" "extraTrees" ## [49] "fda" "FH.GBML" "FIR.DM" ## [52] "foba" "FRBCS.CHI" "FRBCS.W" ## [55] "FS.HGD" "gam" "gamboost" ## [58] "gamLoess" "gamSpline" "gaussprLinear" ## [61] "gaussprPoly" "gaussprRadial" "gbm_h3o" ## [64] "gbm" "gcvEarth" "GFS.FR.MOGUL" ## [67] "GFS.GCCL" "GFS.LT.RS" "GFS.THRIFT" ## [70] "glm.nb" "glm" "glmboost" ## [73] "glmnet_h3o" "glmnet" "glmStepAIC" ## [76] "gpls" "hda" "hdda" ## [79] "hdrda" "HYFIS" "icr" ## [82] "J48" "JRip" "kernelpls" ## [85] "kknn" "knn" "krlsPoly" ## [88] "krlsRadial" "lars" "lars2" ## [91] "lasso" "lda" "lda2" ## [94] "leapBackward" "leapForward" "leapSeq" ## [97] "Linda" "lm" "lmStepAIC" ## [100] "LMT" "loclda" "logicBag" ## [103] "LogitBoost" "logreg" "lssvmLinear" ## [106] "lssvmPoly" "lssvmRadial" "lvq" ## [109] "M5" "M5Rules" "manb" ## [112] "mda" "Mlda" "mlp" ## [115] "mlpKerasDecay" "mlpKerasDecayCost" "mlpKerasDropout" ## [118] "mlpKerasDropoutCost" "mlpML" "mlpSGD" ## [121] "mlpWeightDecay" "mlpWeightDecayML" "monmlp" ## [124] "msaenet" "multinom" "mxnet" ## [127] "mxnetAdam" "naive_bayes" "nb" ## [130] "nbDiscrete" "nbSearch" "neuralnet" ## [133] "nnet" "nnls" "nodeHarvest" ## [136] "null" "OneR" "ordinalNet" ## [139] "ORFlog" "ORFpls" "ORFridge" ## [142] "ORFsvm" "ownn" "pam" ## [145] "parRF" "PART" "partDSA" ## [148] "pcaNNet" "pcr" "pda" ## [151] "pda2" "penalized" "PenalizedLDA" ## [154] "plr" "pls" "plsRglm" ## [157] "polr" "ppr" "PRIM" ## [160] "protoclass" "pythonKnnReg" "qda" ## [163] "QdaCov" "qrf" "qrnn" ## [166] "randomGLM" "ranger" "rbf" ## [169] "rbfDDA" "Rborist" "rda" ## [172] "regLogistic" "relaxo" "rf" ## [175] "rFerns" "RFlda" "rfRules" ## [178] "ridge" "rlda" "rlm" ## [181] "rmda" "rocc" "rotationForest" ## [184] "rotationForestCp" "rpart" "rpart1SE" ## [187] "rpart2" "rpartCost" "rpartScore" ## [190] "rqlasso" "rqnc" "RRF" ## [193] "RRFglobal" "rrlda" "RSimca" ## [196] "rvmLinear" "rvmPoly" "rvmRadial" ## [199] "SBC" "sda" "sdwd" ## [202] "simpls" "SLAVE" "slda" ## [205] "smda" "snn" "sparseLDA" ## [208] "spikeslab" "spls" "stepLDA" ## [211] "stepQDA" "superpc" "svmBoundrangeString"## [214] "svmExpoString" "svmLinear" "svmLinear2" ## [217] "svmLinear3" "svmLinearWeights" "svmLinearWeights2" ## [220] "svmPoly" "svmRadial" "svmRadialCost" ## [223] "svmRadialSigma" "svmRadialWeights" "svmSpectrumString" ## [226] "tan" "tanSearch" "treebag" ## [229] "vbmpRadial" "vglmAdjCat" "vglmContRatio" ## [232] "vglmCumulative" "widekernelpls" "WM" ## [235] "wsrf" "xgbLinear" "xgbTree" ## [238] "xyf"
