bootstrap
Attributes
- Graph
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- Supertypes
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class Objecttrait Matchableclass Any
- Self type
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bootstrap.type
Members list
Value members
Concrete methods
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.
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.
Value parameters
- k
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k-round bootstrap estimation.
- 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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the error rates of each round.
Bootstrap validation on a data frame classifier.
Bootstrap validation on a data frame classifier.
Value parameters
- data
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data samples.
- k
-
k-round bootstrap estimation.
- trainer
-
a code block to return a classifier trained on the given data.
Attributes
- Returns
-
the error rates of each round.
Bootstrap validation on a generic regression model.
Bootstrap validation on a generic regression model.
Value parameters
- k
-
k-round bootstrap estimation.
- 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
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the root mean squared error of each round.
Bootstrap validation on a data frame regression model.
Bootstrap validation on a data frame regression model.
Value parameters
- data
-
data samples.
- k
-
k-round bootstrap estimation.
- trainer
-
a code block to return a regression model trained on the given data.
Attributes
- Returns
-
the root mean squared error of each round.