C++ API Reference for Intel® Data Analytics Acceleration Library 2020 Update 1

Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
[detail level 123456789]
\Ndaal
 oNalgorithmsContains classes that implement algorithms for data analysis(data mining), and data modeling(training and prediction). These algorithms include matrix decompositions, clustering algorithms, classification and regression algorithms, as well as association rules discovery
 |oNadaboostContains classes for the AdaBoost classification algorithm
 |oNassociation_rulesContains classes for the association rules algorithm
 |oNbacon_outlier_detectionContains classes for computing the BACON outlier detection
 |oNboostingContains classes of boosting classification algorithms
 |oNbrownboostContains classes for the BrownBoost classification algorithm
 |oNcholeskyContains classes for computing Cholesky decomposition
 |oNclassifierContains classes for working with classifiers
 |oNcorrelation_distanceContains classes for computing the correlation distance
 |oNcosine_distanceContains classes for computing the cosine distance
 |oNcovarianceContains classes for computing the correlation or variance-covariance matrix
 |oNdbscanContains classes of the DBSCAN algorithm
 |oNdecision_forestContains classes of the decision forest algorithm
 |oNdecision_treeContains classes for Decision tree algorithm
 |oNdistributionsContains classes for distributions
 |oNelastic_netContains classes of the elastic net algorithm
 |oNem_gmmContains classes for the EM for GMM algorithm
 |oNenginesContains classes for engines
 |oNgbtContains classes of the gradient boosted trees algorithm
 |oNimplicit_alsContains classes of the implicit ALS algorithm
 |oNinterface1Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface
 |oNkdtree_knn_classificationContains classes for KD-tree based kNN algorithm
 |oNkernel_functionContains classes for computing kernel functions
 |oNkmeansContains classes of K-Means algorithm
 |oNlasso_regressionContains classes of the lasso regression algorithm
 |oNlinear_modelContains classes of the regression algorithm
 |oNlinear_regressionContains classes of the linear regression algorithm
 |oNlogistic_regressionContains classes for the logistic regression algorithm
 |oNlogitboostContains classes for the LogitBoost classification algorithm
 |oNlow_order_momentsContains classes for computing the results of the low order moments algorithm
 |oNmathContains classes for computing math functions
 |oNmulti_class_classifierContains classes for computing the results of the multi-class classifier algorithm
 |oNmultinomial_naive_bayesContains classes for multinomial Naive Bayes algorithm
 |oNmultivariate_outlier_detectionContains classes for computing the multivariate outlier detection
 |oNneural_networksContains classes for training and prediction using neural network
 |oNnormalizationContains classes to run the min-max normalization algorithms
 |oNoptimization_solverContains classes for optimization solver algorithms
 |oNpcaContains classes for computing the results of the principal component analysis (PCA) algorithm
 |oNpivoted_qrContains classes for computing the pivoted QR decomposition
 |oNqrContains classes for computing the results of the QR decomposition algorithm
 |oNquality_metricContains classes to compute quality metrics
 |oNquality_metric_setContains classes to compute a quality metric set
 |oNquantilesContains classes to run the quantile algorithms
 |oNregressionContains base classes for the regression algorithms
 |oNridge_regressionContains classes of the ridge regression algorithm
 |oNsortingContains classes to run the sorting algorithms
 |oNstumpContains classes to work with the decision stump training algorithm
 |oNsvdContains classes to run the singular-value decomposition (SVD) algorithm
 |oNsvmContains classes to work with the support vector machine classifier
 |oNtree_utils
 |oNunivariate_outlier_detectionContains classes for computing results of the univariate outlier detection algorithm
 |oNweak_learnerContains classes for working with weak learners
 |oCAnalysisContainerIfaceAbstract interface class that provides virtual methods to access and run implementations of the analysis algorithms. It is associated with the Analysis class and supports the methods for computation and finalization of the analysis results in the batch, distributed, and online modes. The methods of the container are defined in derivative containers defined for each algorithm of data analysis
 |oCAnalysisProvides methods for execution of operations over data, such as computation of Summary Statistics estimates. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of the data analysis are derived classes of the Analysis class. The class additionally provides virtual methods for validation of input and output parameters of the algorithms
 |oCPredictionContainerIfaceAbstract interface class that provides virtual methods to access and run implementations of the algorithms for model based prediction. Is associated with the Prediction class and supports the methods for computing the prediction results based on the trained model. The methods of the container are defined in derivative containers defined for each prediction algorithm
 |oCDistributedPredictionContainerIface
 |oCPredictionProvides prediction methods depending on the model such as linear_regression::Model. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of the model based data prediction are derived classes of the Prediction class. The class additionally provides virtual methods for validation of input and output parameters of the algorithms
 |oCDistributedPrediction
 |oCTrainingContainerIfaceAbstract interface class that provides virtual methods to access and run implementations of the model training algorithms. The class is associated with the Training class and supports the methods for computation and finalization of the training output in the batch, distributed, and online modes. The methods of the container are defined in derivative containers defined for each training algorithm
 |\CTrainingProvides methods to train models that depend on the data provided. For example, these methods enable training the linear regression model. The methods of the class support different computation modes: batch, distributed, and online(see ComputeMode). Classes that implement specific algorithms of model training are derived classes of the Training class. The class additionally provides methods for validation of input and output parameters of the algorithms
 oNdata_managementContains classes that implement data management functionality, including NumericTables, DataSources, and Compression
 |oNdata_feature_utilsContains service functionality that simplifies feature handling
 |oNfeaturesContains service functionality that simplifies feature handling
 |oNinterface1Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface
 |oNinternal
 |oNmodifiersContains modifiers components for different Data Sources
 |oCFeatureAuxDataStructure for auxiliary data used for feature extraction
 |oCModifierIfaceAbstract interface class that defines the interface for a features modifier
 |oCMakeCategoricalMethods of the class to set a feature categorical
 |oCOneHotEncoderMethods of the class to set a feature binary categorical
 |\CColumnFilterMethods of the class to filter out data source features from output numeric table
 oNservicesContains classes that implement service functionality, including error handling, memory allocation, and library version information
 |oNinterface1Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface
 |\Ninternal
 oCBaseBase class for Intel(R) Data Analytics Acceleration Library objects
 oCIsSameType
 \CIsSameType< U, U >

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