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

Namespace List
Here is a list of all documented namespaces with brief descriptions:
[detail level 12345678]
\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
 |\Nweak_learnerContains classes for working with weak learners
 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
 |\NmodifiersContains modifiers components for different Data Sources
 \NservicesContains classes that implement service functionality, including error handling, memory allocation, and library version information
  \Ninterface1Contains version 1.0 of Intel(R) Data Analytics Acceleration Library (Intel(R) DAAL) interface

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