xgboost
Namespace List
Here is a list of all namespaces with brief descriptions:
[detail level 1234]
 Ndmlc
 Nserializer
 CHandler< xgboost::Entry >
 Nstd
 Chash< xgboost::IntrusivePtr< T > >
 NxgboostNamespace of xgboost
 Ncollective
 CSockAddrV6
 CSockAddrV4
 CSockAddressAddress for TCP socket, can be either IPv4 or IPv6
 CTCPSocketTCP socket for simple communication
 Ncommon
 Ndetail
 CSpanSpan class implementation, based on ISO++20 span<T>. The interface should be the same
 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
 Ngbm
 Nlinalg
 Ndetail
 CTensorViewA tensor view with static type and dimension. It implements indexing and slicing
 CTensorA tensor storage. To use it for other functionality like slicing one needs to obtain a view first. This way we can use it on both host and device
 Nsystem
 CGradientPairInt64Fixed point representation for high precision gradient pair. Has a different interface so we don't accidentally use it in gain calculations
 CMetaInfoMeta information about dataset, always sit in memory
 CEntryElement from a sparse vector
 CBatchParamParameters for constructing batches
 CHostSparsePageView
 CSparsePageIn-memory storage unit of sparse batch, stored in CSR format
 CCSCPage
 CExtSparsePageSparse page for exporting DMatrix. Same as SparsePage, just a different type to prevent being used internally
 CSortedCSCPage
 CEllpackPageA page stored in ELLPACK format
 CBatchIteratorImpl
 CBatchIterator
 CBatchSet
 CDMatrixInternal data structured used by XGBoost during training
 CFeatureMapFeature map data structure to help text model dump. TODO(tqchen) consider make it even more lightweight
 CGradientBoosterInterface of gradient boosting model
 CGradientBoosterRegRegistry entry for tree updater
 CGenericParameter
 CGlobalConfiguration
 CHostDeviceVectorImpl
 CHostDeviceVector
 CIntrusivePtrCellHelper class for embedding reference counting into client objects. See https://www.boost.org/doc/libs/1_74_0/doc/html/atomic/usage_examples.html for discussions of memory order
 CIntrusivePtrImplementation of Intrusive Pointer. A smart pointer that points to an object with an embedded reference counter. The underlying object must implement a friend function IntrusivePtrRefCount() that returns the ref counter (of type IntrusivePtrCell). The intrusive pointer is faster than std::shared_ptr<>: std::shared_ptr<> makes an extra memory allocation for the ref counter whereas the intrusive pointer does not
 CHash
 CValue
 CJsonString
 CJsonArray
 CJsonTypedArrayTyped array for Universal Binary JSON
 CJsonObject
 CJsonNumber
 CJsonInteger
 CJsonNull
 CJsonBooleanDescribes both true and false
 CJsonData structure representing JSON format
 CJsonReader
 CSourceLocation
 CJsonWriter
 CUBJReaderReader for UBJSON https://ubjson.org/
 CUBJWriterWriter for UBJSON https://ubjson.org/
 CXGBAPIThreadLocalEntryEntry to to easily hold returning information
 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
 CLearnerModelParam
 CLinearUpdaterInterface of linear updater
 CLinearUpdaterRegRegistry entry for linear updater
 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"
 CModel
 CConfigurable
 CObjFunctionInterface of objective function
 CObjFunctionRegRegistry entry for objective factory functions
 CXGBoostParameter
 CPredictionCacheEntryContains pointer to input matrix and associated cached predictions
 CPredictionContainer
 CPredictorPerforms prediction on individual training instances or batches of instances for GBTree. 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
 CPredictorRegRegistry entry for predictor
 CStringView
 CObjInfoA struct returned by objective, which determines task at hand. The struct is not used by any algorithm yet, only for future development like categorical split
 CTreeParamMeta parameters of the tree
 CRTreeNodeStatNode statistics used in regression tree
 CRegTreeDefine regression tree to be the most common tree model. This is the data structure used in xgboost's major tree models
 CCategoricalSplitMatrix
 CFVecDense feature vector that can be taken by RegTree and can be construct from sparse feature vector
 CNodeTree node
 CSegment
 CTreeUpdaterInterface of tree update module, that performs update of a tree
 CTreeUpdaterRegRegistry entry for tree updater