TreeEnsembleFeaturizationEstimatorBase Class

Definition

Namespace:

Microsoft.ML.Trainers.FastTree

Assembly:

Microsoft.ML.FastTree.dll

Package:

Microsoft.ML.FastTree v5.0.0-preview.1.25125.4

Source:

TreeEnsembleFeaturizationEstimator.cs

This class encapsulates the common behavior of all tree-based featurizers such as FastTreeBinaryFeaturizationEstimator, FastForestBinaryFeaturizationEstimator, FastTreeRegressionFeaturizationEstimator, FastForestRegressionFeaturizationEstimator, and PretrainedTreeFeaturizationEstimator. All tree-based featurizers share the same output schema computed by GetOutputSchema(SchemaShape). All tree-based featurizers requires an input feature column name and a suffix for all output columns. The ITransformer returned by Fit(IDataView) produces three columns: (1) the prediction values of all trees, (2) the IDs of leaves the input feature vector falling into, and (3) the binary vector which encodes the paths to those destination leaves.

C#

public abstract class TreeEnsembleFeaturizationEstimatorBase : Microsoft.ML.IEstimator<Microsoft.ML.Trainers.FastTree.TreeEnsembleFeaturizationTransformer>

Inheritance

Object

TreeEnsembleFeaturizationEstimatorBase

Derived

Microsoft.ML.Trainers.FastTree.FastForestBinaryFeaturizationEstimator

Microsoft.ML.Trainers.FastTree.FastForestRegressionFeaturizationEstimator

Microsoft.ML.Trainers.FastTree.FastTreeBinaryFeaturizationEstimator

Microsoft.ML.Trainers.FastTree.FastTreeRankingFeaturizationEstimator

Microsoft.ML.Trainers.FastTree.FastTreeRegressionFeaturizationEstimator

Microsoft.ML.Trainers.FastTree.FastTreeTweedieFeaturizationEstimator

Microsoft.ML.Trainers.FastTree.PretrainedTreeFeaturizationEstimator

Implements

IEstimator<TreeEnsembleFeaturizationTransformer>

Methods

Fit(IDataView)	Produce a TreeEnsembleModelParameters which maps the column called InputColumnName in input to three output columns.
GetOutputSchema(SchemaShape)	PretrainedTreeFeaturizationEstimator adds three float-vector columns into inputSchema. Given a feature vector column, the added columns are the prediction values of all trees, the leaf IDs the feature vector falls into, and the paths to those leaves.

Extension Methods

AppendCacheCheckpoint<TTrans>(IEstimator<TTrans>, IHostEnvironment)

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.

WithOnFitDelegate<TTransformer>(IEstimator<TTransformer>, Action<TTransformer>)

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.

Applies to