Bootstrap validation on a data frame classifier.
Bootstrap validation on a data frame classifier.
k-round bootstrap estimation.
data samples.
a code block to return a classifier trained on the given data.
the error rates of each round.
Bootstrap validation on a generic classifier.
Bootstrap validation on a generic classifier. The bootstrap is a general tool for assessing statistical accuracy. The basic idea is to randomly draw datasets with replacement from the training data, each sample the same size as the original training set. This is done many times (say k = 100), producing k bootstrap datasets. Then we refit the model to each of the bootstrap datasets and examine the behavior of the fits over the k replications.
k-round bootstrap estimation.
data samples.
sample labels.
a code block to return a classifier trained on the given data.
the error rates of each round.
Bootstrap validation on a data frame regression model.
Bootstrap validation on a data frame regression model.
k-round bootstrap estimation.
data samples.
a code block to return a regression model trained on the given data.
the root mean squared error of each round.
Bootstrap validation on a generic regression model.
Bootstrap validation on a generic regression model.
data samples.
response variable.
k-round bootstrap estimation.
validation measures such as MSE, AbsoluteDeviation, etc.
a code block to return a regression model trained on the given data.
the root mean squared error of each round.