Say that I have traindf with dimensions:
> dim(traindf)
[1] 5000 25
And I want to extract the a useful logistic regression model.
For it I have used caret code below using backward stepwise selection with 10-fold cross-validation.
trControl <- trainControl(method="cv", # K-folk Cross-validation
number = 10, # K = 10
savePredictions = T,
classProbs = T,
verboseIter = T,
summaryFunction = twoClassSummary)
caret_model <- train(Class~.,
traindf,
method="glmStepAIC", # Step wise AIC
family="binomial", # Logistic regression is specified
direction="backward", # Backward selection
trace = F,
trControl=trControl)
The code works properly, it returns a model with 0.86 ROC.
My questions are on how the algorithm works.
1- I'm not sure whether stepwise selection selects, for each model with k-variables, the model with lowest deviance or AIC?
2- Does the algorithm cross-validate the best model from each k-variables and output the best from all of those or just cross-validate the best model based on AIC from step-wise selection?
caret method glmStepAIC internally calls MASS::stepAIC, therefore the answer to your first question is AIC is used for selection of variables.
To answer your second question. Caret partitions the data as you define in trainControl, which is in your case 10-fold CV. For each of the 10 training sets glmStepAIC is run, it selects the best model based on AIC and this model is used to predict on the respective CV test sets. The average performance of these predictions is reported under caret_model$results. After this a glmStepAIC is run on all of the supplied data and the optimal model based on AIC is selected, this model is stored in caret_model$finalModel and used to predict on new data.
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'm working with caret and the method avNNET. I would like to try all subsets of variables while doing cross validation. So I can determine the best predictors and parameters (like a brute-force approach).
I have used stepAIC with glm, is there something similar?
In the caret manual you will find the "pcaNNet" method, which is Neural Networks with Feature Extraction.
An example using it:
# define training control
train_control <- trainControl(method="repeatedcv", number=10, repeats = 10, classProbs = TRUE)
# train the model
model <- train(Status~., data=My_data, trControl=train_control, method="pcaNNet", metric = "Kappa")
# summarize results
print(model)
# Confusion matrix
model %>% confusionMatrix()