Concatenating Estimator Class
Concatenates one or more input columns into a new output column.
public sealed class ColumnConcatenatingEstimator : Microsoft.ML.IEstimator<Microsoft.ML.Data.ColumnConcatenatingTransformer>
type ColumnConcatenatingEstimator = class interface IEstimator<ColumnConcatenatingTransformer>
Public NotInheritable Class ColumnConcatenatingEstimator Implements IEstimator(Of ColumnConcatenatingTransformer)
|Does this estimator need to look at the data to train its parameters?||No|
|Input column data type||Any, except key type. All input columns must have the same type.|
|Output column data type||A vector of the input columns' data type|
The resulting ColumnConcatenatingTransformer creates a new column, named as specified in the output column name parameters, where the input values are concatenated in a vector. The order of the concatenation follows the order in which the input columns are specified.
If the input columns' data type is a vector the output column data type remains the same. However, the size of the vector will be the sum of the sizes of the input vectors.
Check the See Also section for links to usage examples.
Trains and returns a ColumnConcatenatingTransformer.
Returns the SchemaShape of the schema which will be produced by the transformer. Used for schema propagation and verification in a pipeline.
Given an estimator, return a wrapping object that will call a delegate once Fit(IDataView) is called. It is often important for an estimator to return information about what was fit, which is why the Fit(IDataView) method returns a specifically typed object, rather than just a general ITransformer. However, at the same time, IEstimator<TTransformer> are often formed into pipelines with many objects, so we may need to build a chain of estimators via EstimatorChain<TLastTransformer> where the estimator for which we want to get the transformer is buried somewhere in this chain. For that scenario, we can through this method attach a delegate that will be called once fit is called.