loocv
Attributes
- Graph
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- Supertypes
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class Objecttrait Matchableclass Any
- Self type
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loocv.type
Members list
Value members
Concrete methods
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.
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.
Value parameters
- trainer
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a code block to return a classifier trained on the given data.
- x
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data samples.
- y
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sample labels.
Attributes
- Returns
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metric scores.
Leave-one-out cross validation on a data frame classifier.
Leave-one-out cross validation on a data frame classifier.
Value parameters
- data
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data samples.
- formula
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model formula.
- trainer
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a code block to return a classifier trained on the given data.
Attributes
- Returns
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metric scores.
Leave-one-out cross validation on a generic regression model.
Leave-one-out cross validation on a generic regression model.
Value parameters
- trainer
-
a code block to return a regression model trained on the given data.
- x
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data samples.
- y
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response variable.
Attributes
- Returns
-
metric scores.
Leave-one-out cross validation on a data frame regression model.
Leave-one-out cross validation on a data frame regression model.
Value parameters
- data
-
data samples.
- formula
-
model formula.
- trainer
-
a code block to return a regression model trained on the given data.
Attributes
- Returns
-
metric scores.