Chain<TLast Transformer> Class
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Represents a chain (potentially empty) of estimators that end with a
If the chain is empty,
TLastTransformer is always ITransformer.
public sealed class EstimatorChain<TLastTransformer> : Microsoft.ML.IEstimator<Microsoft.ML.Data.TransformerChain<TLastTransformer>> where TLastTransformer : class, ITransformer
type EstimatorChain<'LastTransformer (requires 'LastTransformer : null and 'LastTransformer :> ITransformer)> = class interface IEstimator<TransformerChain<'LastTransformer>>
Public NotInheritable Class EstimatorChain(Of TLastTransformer) Implements IEstimator(Of TransformerChain(Of TLastTransformer))
Create an empty estimator chain.
Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers or feature engineering that take multiple data passes. It is also helpful to have after a slow operation, for example after dataset loading from a slow source or after feature engineering that is slow on its apply phase, if downstream estimators will do multiple passes over the output of this operation. Adding a cache checkpoint at the begin or end of an EstimatorChain<TLastTransformer> is meaningless and should be avoided. Cache checkpoints should be removed if disk thrashing or OutOfMemory exceptions are seen, which can occur on when the featured dataset immediately prior to the checkpoint is larger than available RAM.
Append a 'caching checkpoint' to the estimator chain. This will ensure that the downstream estimators will be trained against cached data. It is helpful to have a caching checkpoint before trainers that take multiple data passes.
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.
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