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Reference documentation and code samples for the Vertex AI V1 API class Google::Cloud::AIPlatform::V1::StratifiedSplit.
Assigns input data to the training, validation, and test sets so that the
distribution of values found in the categorical column (as specified by the
key field) is mirrored within each split. The fraction values determine
the relative sizes of the splits.
For example, if the specified column has three values, with 50% of the rows having value "A", 25% value "B", and 25% value "C", and the split fractions are specified as 80/10/10, then the training set will constitute 80% of the training data, with about 50% of the training set rows having the value "A" for the specified column, about 25% having the value "B", and about 25% having the value "C".
Only the top 500 occurring values are used; any values not in the top 500 values are randomly assigned to a split. If less than three rows contain a specific value, those rows are randomly assigned.
Supported only for tabular Datasets.
Inherits
- Object
Extended By
- Google::Protobuf::MessageExts::ClassMethods
Includes
- Google::Protobuf::MessageExts
Methods
#key
def key() -> ::String- (::String) — Required. The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
#key=
def key=(value) -> ::String- value (::String) — Required. The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
- (::String) — Required. The key is a name of one of the Dataset's data columns. The key provided must be for a categorical column.
#test_fraction
def test_fraction() -> ::Float- (::Float) — The fraction of the input data that is to be used to evaluate the Model.
#test_fraction=
def test_fraction=(value) -> ::Float- value (::Float) — The fraction of the input data that is to be used to evaluate the Model.
- (::Float) — The fraction of the input data that is to be used to evaluate the Model.
#training_fraction
def training_fraction() -> ::Float- (::Float) — The fraction of the input data that is to be used to train the Model.
#training_fraction=
def training_fraction=(value) -> ::Float- value (::Float) — The fraction of the input data that is to be used to train the Model.
- (::Float) — The fraction of the input data that is to be used to train the Model.
#validation_fraction
def validation_fraction() -> ::Float- (::Float) — The fraction of the input data that is to be used to validate the Model.
#validation_fraction=
def validation_fraction=(value) -> ::Float- value (::Float) — The fraction of the input data that is to be used to validate the Model.
- (::Float) — The fraction of the input data that is to be used to validate the Model.