When using caret's train function to fit GBM classification models, the function predictionFunction converts probabilistic predictions into factors based on a probability threshold of 0.5.
out <- ifelse(gbmProb >= .5, modelFit$obsLevels[1], modelFit$obsLevels[2])
## to correspond to gbmClasses definition above
This conversion seems premature if a user is trying to maximize the area under the ROC curve (AUROC). While sensitivity and specificity correspond to a single probability threshold (and therefore require factor predictions), I'd prefer AUROC be calculated using the raw probability output from gbmPredict. In my experience, I've rarely cared about the calibration of a classification model; I want the most informative model possible, regardless of the probability threshold over which the model predicts a '1' vs. '0'. Is it possible to force raw probabilities into the AUROC calculation? This seems tricky, since whatever summary function is used gets passed predictions that are already binary.
"since whatever summary function is used gets passed predictions that are already binary"
That's definitely not the case.
It cannot use the classes to compute the ROC curve (unless you go out of your way to do so). See the note below.
train can predict the classes as factors (using the internal code that you show) and/or the class probabilities.
For example, this code will compute the class probabilities and use them to get the area under the ROC curve:
library(caret)
library(mlbench)
data(Sonar)
ctrl <- trainControl(method = "cv",
summaryFunction = twoClassSummary,
classProbs = TRUE)
set.seed(1)
gbmTune <- train(Class ~ ., data = Sonar,
method = "gbm",
metric = "ROC",
verbose = FALSE,
trControl = ctrl)
In fact, if you omit the classProbs = TRUE bit, you will get the error:
train()'s use of ROC codes requires class probabilities. See the classProbs option of trainControl()
Max
Related
I am rather new to machine learning and I am currently trying to implement a random forest classification using the caret and randomForest packages in R. I am using the trainControl function with repeated cross-validation. Maybe it is a stupid question but as far as I understand random forest usually uses bagging to split the training data into different subsets with replacement using 1/3 as a validation set based on which the OOB is calculated on. But what happens if you specify that you want to use k-fold cross-validation? From the caret documentation, I assumed that it uses only cross-validation for the resampling, But if it only used cross-validation, why do you still get an OOB error? Or is bagging still used for the creation of the model and cross-validation for the performance evaluation?
TrainingControl <- trainControl(method = "repeatedcv", number = 10, repeats = 3, savePredictions = TRUE, classProbs = TRUE, search = "grid")
train(x ~ ., data = training_set,
method = "rf",
metric = "Accuracy",
trControl = TrainingControl,
ntree = 1000,
importance = TRUE
)
Trying to address your questions:
random forest usually uses bagging to split the training data into
different subsets with replacement using 1/3 as a validation set based
on which the OOB is calculated on
Yes, caret is using randomForest() from the package randomForest, and more specifically, it bootstraps on the training data, generate multiple decision tress which are bagged, to reduce overfitting, from wiki:
This bootstrapping procedure leads to better model performance because
it decreases the variance of the model, without increasing the bias.
This means that while the predictions of a single tree are highly
sensitive to noise in its training set, the average of many trees is
not, as long as the trees are not correlated.
So if you call k-fold cross-validation from caret, it simply runs randomForest() on different training sets, therefore the answer to this:
But what happens if you specify that you want to use k-fold
cross-validation? From the caret documentation, I assumed that it uses
only cross-validation for the resampling, But if it only used
cross-validation, why do you still get an OOB error?
Would be the sampling and bagging is performed because it is part of randomforest. caret simply repeats this on different training set and estimates the error on their respective test set. The OOB error generated from randomForest() stays regardless. The difference is that you have a truly "unseen" data that can be used to evaluate your model.
I have the following random forest (regression) model with the default parameters
set.seed(42)
# Define train control
trControl <- trainControl(method = "cv",
number = 10,
search = "grid")
# Random Forest (regression) model
rf_reg <- train(Price.Gas~., data=data_train,
method = "rf",
metric = "RMSE",
trControl = trControl)
This is the output plot of the true values (black) and the predicted values(red)
I'd like the model to perform better by changing its tunning parameters (e.g. ntree, maxnodes, search, etc).
I don't think changing one by one is the most efficient way of doing this.
How could I efficiently test the parameters in R to obtain a better random forest (i.e. one that predicts the data well)?
You will to perform some sort of hyperparameter search (grid or random) where you list all values you want to test (or sequences) and then compute all of them to obtain the best configuration. This links explains the possible aproaches with caret: https://rpubs.com/phamdinhkhanh/389752
I have some r/caret code to fit several cross-validated models to some data, but I'm getting a warning message that I'm having trouble finding any information on. Is this something I should be concerned about?
library(datasets)
library(caret)
library(caretEnsemble)
# load data
data("iris")
# establish cross-validation structure
set.seed(32)
trainControl <- trainControl(method="repeatedcv", number=5, repeats=3, savePredictions=TRUE, search="random")
# fit several (cross-validated) models
algorithmList <- c('lda', # Linear Discriminant Analysis
'rpart' , # Classification and Regression Trees
'svmRadial') # SVM with RBF Kernel
models <- caretList(Species~., data=iris, trControl=trainControl, methodList=algorithmList)
log output:
Warning messages:
1: In trControlCheck(x = trControl, y = target) :
x$savePredictions == TRUE is depreciated. Setting to 'final' instead.
2: In trControlCheck(x = trControl, y = target) :
indexes not defined in trControl. Attempting to set them ourselves, so each model in the ensemble will have the same resampling indexes.
...I thought my trainControl object, defining a cross-validation structure (of 3x 5-fold cross-validation) would generate a set of indices for the cv splits. So I'm confused why I would get this message.
trainControl does not by default generate you the indices, it acts as a way of passing all the parameters to each model you are training.
When we search github issues regarding the error we can find this particular issue.
You need to make sure that every model is fit with the EXACT same
resampling folds. caretEnsemble builds the ensemble by merging
together the test sets for each cross-validation fold, and you will
get incorrect results if each fold has different observations in it.
Before you fit your models, you need to construct a trainControl
object, and manually set the indexes in that object.
E.g. myControl <- trainControl(index=createFolds(y, 10)).
We are working on an interface to caretEnsemble that handles
constructing the resampling strategy for you and then fitting multiple
models using those resamples, but it is not yet finished.
To reiterate, that check is there for a reason. You need to set the
index argument in trainControl, and pass the EXACT SAME indexes to
each model you wish to ensemble.
So what that means is when you specify number = 5 and repeats = 3 the models aren't actually getting a predetermined index for what samples belong to each fold but are rather generating their own independently.
Therefore to ensure that the models are consistent with one another regarding which samples belong to which folds you must specify index = createFolds(iris$Species, 5) in your trainControl object
# new trainControl object with index specified
trainControl <- trainControl(method = "repeatedcv",
number = 5,
index = createFolds(iris$Species, 5),
repeats = 3,
savePredictions = "all",
search = "random")
I have a dataset that is publicly available ("banknote_authentication"). It has four predictor variables (variance, skewness, entropy and kurtosis) and one target variable (class). The dataset contains 1372 records. I am using R version 3.3.2 and RStudio on a Windows machine.
I'm using the caret package to create a cross-validation approach for the following models: logistic regression, LDA, QDA, KNN with k=1,2,5,10,20,50,100. I need to obtain the test error as well as sensitivity and specificity for each of these methods and present the results in the form of boxplots, compare test error/sensitivity/specificity across these methods. Here is an example of my logistic regression code:
ctrl <- trainControl(method = "repeatedcv", number = 10, savePredictions = TRUE)
mod_fit <- train(class_label~variance+skewness+kurtosis+entropy, data=SBank, method="glm", family="binomial", trControl = ctrl, tuneLength = 5)
pred = predict(mod_fit, newdata=SBank)
And here is how I am evaluating my models:
confusionMatrix(data=pred, SBank$class_label)
How do I extract the accuracy, sensitivity and specificity metrics from the test dataset so that I can create the boxplots? I do not need the summary metrics that are output from the confusion matrix, I need a dataset of these metrics that I can represent graphically.
I am quite new to the neural network world so I ask for your understanding. I am generating some tests and thus I have a question about the parameters size and decay. I use the caret package and the method nnet. Example dataset:
require(mlbench)
require(caret)
require (nnet)
data(Sonar)
mydata=Sonar[,1:12]
set.seed(54878)
ctrl = trainControl(method="cv", number=10,returnResamp = "all")
for_train= createDataPartition(mydata$V12, p=.70, list=FALSE)
my_train=mydata[for_train,]
my_test=mydata[-for_train,]
t.grid=expand.grid(size=5,decay=0.2)
mymodel = train(V12~ .,data=my_train,method="nnet",metric="Rsquared",trControl=ctrl,tuneGrid=t.grid)
So, two are my questions. First, is this the best way with caret to use the nnet method?Second, I have read about the size and the decay (eg. Purpose of decay parameter in nnet function in R?) but I cannot understand how to use them in practice here. Can anyone help?
Brief Caret explanation
The Caret package lets you train different models and tuning hyper-parameters using Cross Validation (Hold-Out or K-fold) or Bootstrap.
There are two different ways to tune the hyper-parameters using Caret: Grid Search and Random Search. If you use Grid Search (Brute Force) you need to define the grid for every parameter according to your prior knowledge or you can fix some parameters and iterate on the remain ones. If you use Random Search you need to specify a tuning length (maximum number of iterations) and Caret is going to use random values for hyper-parameters until the stop criteria holds.
No matter what method you choose Caret is going to use each combination of hyper-parameters to train the model and compute performance metrics as follows:
Split the initial Training samples into two different sets: Training and Validation (For bootstrap or Cross validation) and into k sets (For k-fold Cross Validation).
Train the model using the training set and to predict on validation set (For Cross Validation Hold-Out and Bootstrap). Or using k-1 training sets and to predict using the k-th training set (For K-fold Cross Validation).
On the validation set Caret computes some performance metrics as ROC, Accuracy...
Once the Grid Search has finished or the Tune Length is completed Caret uses the performance metrics to select the best model according to the criteria previously defined (You can use ROC, Accuracy, Sensibility, RSquared, RMSE....)
You can create some plot to understand the resampling profile and to pick the best model (Keep in mind performance and complexity)
if you need more information about Caret you can check the Caret web page
Neural Network Training Process using Caret
When you train a neural network (nnet) using Caret you need to specify two hyper-parameters: size and decay. Size is the number of units in hidden layer (nnet fit a single hidden layer neural network) and decay is the regularization parameter to avoid over-fitting. Keep in mind that for each R package the name of the hyper-parameters can change.
An example of training a Neural Network using Caret for classification:
fitControl <- trainControl(method = "repeatedcv",
number = 10,
repeats = 5,
classProbs = TRUE,
summaryFunction = twoClassSummary)
nnetGrid <- expand.grid(size = seq(from = 1, to = 10, by = 1),
decay = seq(from = 0.1, to = 0.5, by = 0.1))
nnetFit <- train(Label ~ .,
data = Training[, ],
method = "nnet",
metric = "ROC",
trControl = fitControl,
tuneGrid = nnetGrid,
verbose = FALSE)
Finally, you can make some plots to understand the resampling results. The following plot was generated from a GBM training process
GBM Training Process using Caret