Leave-one-out cross validation on a data frame classifier.
Leave-one-out cross validation on a data frame classifier.
model formula.
data samples.
validation measures such as accuracy, specificity, etc.
a code block to return a classifier trained on the given data.
measure results.
Leave-one-out cross validation on a generic classifier.
Leave-one-out cross validation on a generic classifier. LOOCV uses a single observation from the original sample as the validation data, and the remaining observations as the training data. This is repeated such that each observation in the sample is used once as the validation data. This is the same as a K-fold cross-validation with K being equal to the number of observations in the original sample. Leave-one-out cross-validation is usually very expensive from a computational point of view because of the large number of times the training process is repeated.
data samples.
sample labels.
validation measures such as accuracy, specificity, etc.
a code block to return a classifier trained on the given data.
measure results.
Leave-one-out cross validation on a data frame regression model.
Leave-one-out cross validation on a data frame regression model.
model formula.
data samples.
validation measures such as accuracy, specificity, etc.
a code block to return a regression model trained on the given data.
measure results.
Leave-one-out cross validation on a generic regression model.
Leave-one-out cross validation on a generic regression model.
data samples.
response variable.
validation measures such as MSE, AbsoluteDeviation, etc.
a code block to return a regression model trained on the given data.
measure results.