TnT_Split
The TnT_Split object provides methods for splitting datasets into TESTING-sets (.) and TRAINING-sets (-). There are three options on how to split the full dataset: (1) select at RANDOM n_test indices for the test-set (@see tnT_SplitTest) | -.--.---.--.----.--.---.-----.| (2) select the FIRSTn_testindices for the test-set (@see tnT_SplitTest2) | test-set | training-set | (3) select the LAST n_test indices for the test-set (@see tnT_SplitTest3`) | training-set | test-set |
Attributes
- Graph
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- Supertypes
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class Objecttrait Matchableclass Any
- Self type
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TnT_Split.type
Members list
Value members
Concrete methods
Split the dataset given as a combined data-response matrix into a testing-set and training-set based on the given indices.
Split the dataset given as a combined data-response matrix into a testing-set and training-set based on the given indices.
Value parameters
- idx
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the indices for the testing-set
- xy
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the combined data-response matrix
Attributes
Split the dataset given as a data matrix and a response vector into a testing-set and training-set based on the given indices.
Split the dataset given as a data matrix and a response vector into a testing-set and training-set based on the given indices.
Value parameters
- idx
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the set of indices for the testing-set
- x
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the input/data matrix (for some models this may be null => return (null, null)
- y
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the output/response vector
Attributes
- See also
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scalation.modeling.Predictor
Split the dataset given as a data matrix and an integer-valued response vector into a testing-set and training-set based on the given indices.
Split the dataset given as a data matrix and an integer-valued response vector into a testing-set and training-set based on the given indices.
Value parameters
- idx
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the indices for the testing-set
- x
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the input/data matrix (for some models this may be null => return (null, null)
- y
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the integer-valued output/response vector
Attributes
- See also
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scalation.modelingq.classifying.Classifier
Split the dataset given as a data matrix and a response matrix into a testing-set and training-set based on the given indices.
Split the dataset given as a data matrix and a response matrix into a testing-set and training-set based on the given indices.
Value parameters
- idx
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the indices for the testing-set
- x
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the input/data matrix
- y
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the output/response matrix
Attributes
- See also
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scalation.modeling.neuralnet.PredictorMV
Make a permutation generator for integers from 0 until limit.
Make a permutation generator for integers from 0 until limit.
Value parameters
- limit
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the upper limit of integers (exclusive)
- stream
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the random number stream to use
Attributes
Return the indices for the test-set. If rando = false, pick the FIRST indices.
Return the indices for the test-set. If rando = false, pick the FIRST indices.
Value parameters
- n_test
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the size of test-set
- rando
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whether to select indices randomly or in blocks (defaults to true)
Attributes
Return the indices for the test-set. If rando = false, pick the LAST indices.
Return the indices for the test-set. If rando = false, pick the LAST indices.
Value parameters
- n_test
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the size of test-set
- n_total
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the size of full dataset (train plus test)
- rando
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whether to select indices randomly or in blocks (defaults to true)