xgboost
Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 12345]
 NxgboostNamespace of xgboost
 Ncommon
 Ndetail
 CExtentAsBytesValue
 CExtentValue
 CGreater
 CIsAllowedElementTypeConversion
 CIsAllowedExtentConversion
 CIsSpan
 CIsSpanOracle
 CIsSpanOracle< Span< T, Extent > >
 CLess
 CSpanIterator
 CBase64InStreamStream that reads from base64, note we take from file pointers
 CBase64OutStreamStream that write to base64, note we take from file pointers
 CBitMapBit map that contains set of bit indicators
 CColumnColumn storage, to be used with ApplySplit. Note that each bin id is stored as index[i] + index_base
 CColumnMatrixCollection of columns, with support for construction from GHistIndexMatrix
 CColumnSamplerHandles selection of columns due to colsample_bytree, colsample_bylevel and colsample_bynode parameters. Should be initialised before tree construction and to reset when tree construction is completed
 CCompressedBufferWriterWrites bit compressed symbols to a memory buffer. Use CompressedIterator to read symbols back from buffer. Currently limited to a maximum symbol size of 28 bits
 CCompressedIteratorRead symbols from a bit compressed memory buffer. Usable on device and host
 CConfigIteratorIterator that iterates over a configure file and gets the configures
 CConfigReaderBaseBase implementation of config reader
 CConfigStreamReaderIterator use stream base, allows use all types of istream
 CGHistBuilderBuilder for histograms of gradient statistics
 CGHistIndexBlock
 CGHistIndexBlockMatrix
 CGHistIndexMatrixPreprocessed global index matrix, in CSR format Transform floating values to integer index in histogram This is a global histogram index
 CGKQuantileSketchQuantile sketch use WQSummary
 CGKSummaryTraditional GK summary
 CEntryEntry in the sketch summary
 CQueueInput data queue before entering the summary
 CHistCollectionHistogram of gradient statistics for multiple nodes
 CHistCutMatrixCut configuration for all the features
 CMonitorTiming utility used to measure total method execution time over the lifetime of the containing object
 CParallelGroupBuilderMulti-thread version of group builder
 CPeekableInStreamInput stream that support additional PeekRead operation, besides read
 CQuantileSketchTemplateTemplate for all quantile sketch algorithm that uses merge/prune scheme
 CSummaryContainerSame as summary, but use STL to backup the space
 CRange
 CIterator
 CRowSetCollectionCollection of rowset
 CElemData structure to store an instance set, a subset of rows (instances) associated with a particular node in a decision tree
 CSplit
 CSimpleArray
 CSpanSpan class implementation, based on ISO++20 span<T>. The interface should be the same
 CStreamBufferReaderBuffer reader of the stream that allows you to get
 CTimer
 CTransformDo Transformation on HostDeviceVectors
 CWQSummaryExperimental wsummary
 CEntryEntry in the sketch summary
 CQueueInput data queue before entering the summary
 CQEntry
 CWQuantileSketchQuantile sketch use WQSummary
 CWXQSummaryTry to do efficient pruning
 CWXQuantileSketchQuantile sketch use WXQSummary
 Ndetail
 CGradientPairInternalImplementation of gradient statistics pair. Template specialisation may be used to overload different gradients types e.g. low precision, high precision, integer, floating point
 CAllVisibleImpl
 CBatchIterator
 CBatchIteratorImpl
 CBatchSet
 CDataSourceThis is data structure that user can pass to DMatrix::Create to create a DMatrix for training, user can create this data structure for customized Data Loading on single machine
 CDMatrixInternal data structured used by XGBoost during training. There are two ways to create a customized DMatrix that reads in user defined-format
 CEntryElement from a sparse vector
 CFeatureMapFeature map data structure to help text model dump. TODO(tqchen) consider make it even more lightweight
 CGPUDistribution
 CGPUSet
 CGradientBoosterInterface of gradient boosting model
 CGradientBoosterRegRegistry entry for tree updater
 CHostDeviceVector
 CHostDeviceVectorImpl
 CLearnerLearner class that does training and prediction. This is the user facing module of xgboost training. The Load/Save function corresponds to the model used in python/R
 CLinearUpdaterInterface of linear updater
 CLinearUpdaterRegRegistry entry for linear updater
 CMetaInfoMeta information about dataset, always sit in memory
 CMetricInterface of evaluation metric used to evaluate model performance. This has nothing to do with training, but merely act as evaluation purpose
 CMetricRegRegistry entry for Metric factory functions. The additional parameter const char* param gives the value after @, can be null. For example, metric map@3, then: param == "3"
 CObjFunctionInterface of objective function
 CObjFunctionRegRegistry entry for objective factory functions
 CPredictorPerforms prediction on individual training instances or batches of instances for GBTree. The predictor also manages a prediction cache associated with input matrices. If possible, it will use previously calculated predictions instead of calculating new predictions. Prediction functions all take a GBTreeModel and a DMatrix as input and output a vector of predictions. The predictor does not modify any state of the model itself
 CPredictionCacheEntryContains pointer to input matrix and associated cached predictions
 CPredictorRegRegistry entry for predictor
 CRegTreeDefine regression tree to be the most common tree model. This is the data structure used in xgboost's major tree models
 CFVecDense feature vector that can be taken by RegTree and can be construct from sparse feature vector
 CNodeTree node
 CRowSetA vector-like structure to represent set of rows. But saves the memory when all rows are in the set (common case in xgb)
 CRTreeNodeStatNode statistics used in regression tree
 CSparsePageIn-memory storage unit of sparse batch, stored in CSR format
 CTreeParamMeta parameters of the tree
 CTreeUpdaterInterface of tree update module, that performs update of a tree
 CTreeUpdaterRegRegistry entry for tree updater
 CXGBoostBatchCSRMini batch used in XGBoost Data Iteration