Oracle DBMS_DATA_MINING
Version 26ai

General Information
Library Note Morgan's Library Page Header
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Oracle Database 26ai will be available on generic Linux platforms in January and soon on AIX and Windows
Purpose DBMS_DATA_MINING provides routines for Data Mining operations in an Oracle Server supporting both supervised and unsupervised data mining. Supervised data mining predicts a target value based on historical data. Unsupervised data mining discovers natural groupings and does not use a target. You can use Oracle Data Mining to mine structured data and unstructured text.

Supervised data mining functions include:
  • Classification
  • Regression
  • Feature Selection (Attribute Importance)

Unsupervised data mining functions include:
  • Clustering
  • Association
  • Feature Extraction
  • Anomaly Detection
AUTHID CURRENT_USER
Constants -- General Settings - Begin ----------------------------------------

-- Data Prep: Setting Names
prep_auto CONSTANT VARCHAR2(30) := 'PREP_AUTO';

-- Data Prep: Setting Values for prep_auto
prep_auto_off CONSTANT VARCHAR2(30) := 'OFF';
prep_auto_on CONSTANT VARCHAR2(30)  := 'ON';

-- normalization settings
-- 2D numeric columns scale
prep_scale_2dnum CONSTANT VARCHAR2(30)  := 'PREP_SCALE_2DNUM';
-- values for prep_scale_2dnum
prep_scale_stddev CONSTANT VARCHAR2(30) := 'PREP_SCALE_STDDEV';
prep_scale_range CONSTANT VARCHAR2(30) := 'PREP_SCALE_RANGE';
-- nested numeric columns scale
prep_scale_nnum CONSTANT VARCHAR2(30) := 'PREP_SCALE_NNUM';
-- value for prep_scale_nnum
prep_scale_maxabs CONSTANT VARCHAR2(30) := 'PREP_SCALE_MAXABS';
-- 2D numeric shift
prep_shift_2dnum CONSTANT VARCHAR2(30) := 'PREP_SHIFT_2DNUM';
-- values for prep_shift_2dnum
prep_shift_mean CONSTANT VARCHAR2(30) := 'PREP_SHIFT_MEAN';
prep_shift_min CONSTANT VARCHAR2(30) := 'PREP_SHIFT_MIN';

-- Score Criterion Type: Setting Values for score_criterion_type
score_criterion_probability CONSTANT VARCHAR2(30) := 'PROBABILITY';
score_criterion_cost CONSTANT VARCHAR2(30) := 'COST';

-- Row Weights - Setting Name
odms_row_weight_column_name CONSTANT VARCHAR2(30) := 'ODMS_ROW_WEIGHT_COLUMN_NAME';

-- Cost Matrix
cost_matrix_type_score CONSTANT VARCHAR2(30) := 'SCORE';
cost_matrix_type_create CONSTANT VARCHAR2(30) := 'CREATE';

-- Missing Value Treatment - Setting Name
odms_missing_value_treatment CONSTANT VARCHAR2(30) := 'ODMS_MISSING_VALUE_TREATMENT';

-- Missing Value Treatment: Setting Values for ODMS_MISSING_VALUE_TREATMENT
odms_missing_value_mean_mode CONSTANT VARCHAR2(30) := 'ODMS_MISSING_VALUE_MEAN_MODE';
odms_missing_value_delete_row CONSTANT VARCHAR2(30) := 'ODMS_MISSING_VALUE_DELETE_ROW';
odms_missing_value_auto CONSTANT VARCHAR2(30) := 'ODMS_MISSING_VALUE_AUTO';

-- Transactional training data format: Setting Names
odms_item_id_column_name CONSTANT VARCHAR2(30) := 'ODMS_ITEM_ID_COLUMN_NAME';
odms_item_value_column_name CONSTANT VARCHAR2(30) := 'ODMS_ITEM_VALUE_COLUMN_NAME';

-- Unstructured Text Setting Names
odms_text_policy_name CONSTANT VARCHAR2(30) := 'ODMS_TEXT_POLICY_NAME';
odms_text_max_features CONSTANT VARCHAR2(30) := 'ODMS_TEXT_MAX_FEATURES';
odms_text_min_documents CONSTANT VARCHAR2(30) := 'ODMS_TEXT_MIN_DOCUMENTS';

-- Approximate computation
odms_approximate_computation CONSTANT VARCHAR2(30) := 'ODMS_APPROXIMATE_COMPUTATION';
-- Setting values for odms_approximate_computation
odms_appr_comp_enable CONSTANT VARCHAR2(30) := 'ODMS_APPR_COMP_ENABLE';
odms_appr_comp_disable CONSTANT VARCHAR2(30) := 'ODMS_APPR_COMP_DISABLE';

-- Sampling
odms_sampling CONSTANT VARCHAR2(30) := 'ODMS_SAMPLING';
-- Setting values for odms_sampling
odms_sampling_enable CONSTANT VARCHAR2(30) := 'ODMS_SAMPLING_ENABLE';
odms_sampling_disable CONSTANT VARCHAR2(30) := 'ODMS_SAMPLING_DISABLE';

-- Sample size
odms_sample_size CONSTANT VARCHAR2(30) := 'ODMS_SAMPLE_SIZE';

-- Partitioning
odms_partition_columns CONSTANT VARCHAR2(30) := 'ODMS_PARTITION_COLUMNS';

-- Max partition columns
odms_max_partitions CONSTANT VARCHAR2(30) := 'ODMS_MAX_PARTITIONS';

-- Max sup bins ---
clas_max_sup_bins CONSTANT VARCHAR2(30) := 'CLAS_MAX_SUP_BINS';

--Partition build type (inter/intra/hybrid)
odms_partition_build_type CONSTANT VARCHAR2(30) := 'ODMS_PARTITION_BUILD_TYPE';
odms_partition_build_inter CONSTANT VARCHAR2(30) := 'ODMS_PARTITION_BUILD_INTER';
odms_partition_build_intra CONSTANT VARCHAR2(30) := 'ODMS_PARTITION_BUILD_INTRA';
odms_partition_build_hybrid CONSTANT VARCHAR2(30) := 'ODMS_PARTITION_BUILD_HYBRID';

-- random seed
odms_random_seed CONSTANT VARCHAR2(30):= 'ODMS_RANDOM_SEED';

-- retain information for details (default is enable)
odms_details CONSTANT VARCHAR2(30):= 'ODMS_DETAILS';
odms_enable CONSTANT VARCHAR2(30):= 'ODMS_ENABLE';
odms_disable CONSTANT VARCHAR2(30):= 'ODMS_DISABLE';

-- override default tablespace
odms_tablespace_name CONSTANT VARCHAR2(30):= 'ODMS_TABLESPACE_NAME';

-- General Settings - End -------------------------------------------------

----------- Function and Algorithm Settings - Begin ---------------------

-- FUNCTION NAME (input as CREATE_MODEL parameter)
--
classification CONSTANT VARCHAR2(30) := 'CLASSIFICATION';
regression CONSTANT VARCHAR2(30) := 'REGRESSION';
clustering CONSTANT VARCHAR2(30) := 'CLUSTERING';
association CONSTANT VARCHAR2(30) := 'ASSOCIATION';
feature_extraction CONSTANT VARCHAR2(30) := 'FEATURE_EXTRACTION';
attribute_importance CONSTANT VARCHAR2(30) := 'ATTRIBUTE_IMPORTANCE';
time_series CONSTANT VARCHAR2(30) := 'TIME_SERIES';

-- FUNCTION: Setting Names (input to settings_name column in settings table)
clas_priors_table_name CONSTANT VARCHAR2(30) := 'CLAS_PRIORS_TABLE_NAME';
clas_weights_table_name CONSTANT VARCHAR2(30) := 'CLAS_WEIGHTS_TABLE_NAME';
clas_cost_table_name CONSTANT VARCHAR2(30) := 'CLAS_COST_TABLE_NAME';
-- Balanced weights (boolean: on/off) */
clas_weights_balanced CONSTANT VARCHAR2(30) := 'CLAS_WEIGHTS_BALANCED';
clas_weights_bal_off CONSTANT VARCHAR2(30) := 'OFF';
clas_weights_bal_on CONSTANT VARCHAR2(30) := 'ON';

-- AR: Setting Names
asso_max_rule_length CONSTANT VARCHAR2(30) := 'ASSO_MAX_RULE_LENGTH';
asso_min_confidence CONSTANT VARCHAR2(30) := 'ASSO_MIN_CONFIDENCE';
asso_min_support CONSTANT VARCHAR2(30) := 'ASSO_MIN_SUPPORT';
asso_min_support_int CONSTANT VARCHAR2(30) := 'ASSO_MIN_SUPPORT_INT';
asso_min_rev_confidence CONSTANT VARCHAR2(30) := 'ASSO_MIN_REV_CONFIDENCE';
asso_in_rules CONSTANT VARCHAR2(30) := 'ASSO_IN_RULES';
asso_ex_rules CONSTANT VARCHAR2(30) := 'ASSO_EX_RULES';
asso_ant_in_rules CONSTANT VARCHAR2(30) := 'ASSO_ANT_IN_RULES';
asso_ant_ex_rules CONSTANT VARCHAR2(30) := 'ASSO_ANT_EX_RULES';
asso_cons_in_rules CONSTANT VARCHAR2(30) := 'ASSO_CONS_IN_RULES';
asso_cons_ex_rules CONSTANT VARCHAR2(30) := 'ASSO_CONS_EX_RULES';
asso_aggregates CONSTANT VARCHAR2(30) := 'ASSO_AGGREGATES';
asso_abs_error CONSTANT VARCHAR2(30) := 'ASSO_ABS_ERROR';
asso_conf_level CONSTANT VARCHAR2(30) := 'ASSO_CONF_LEVEL';

feat_num_features CONSTANT VARCHAR2(30) := 'FEAT_NUM_FEATURES';
clus_num_clusters CONSTANT VARCHAR2(30) := 'CLUS_NUM_CLUSTERS';

-- ALGORITHM Setting Name (input to settings_name column in settings table)
algo_name CONSTANT VARCHAR2(30) := 'ALGO_NAME';

-- ALGORITHM: Setting Values for algo_name
algo_naive_bayes CONSTANT VARCHAR2(30) := 'ALGO_NAIVE_BAYES';
algo_adaptive_bayes_network CONSTANT VARCHAR2(30) := 'ALGO_ADAPTIVE_BAYES_NETWORK';
algo_support_vector_machines CONSTANT VARCHAR2(30) := 'ALGO_SUPPORT_VECTOR_MACHINES';
algo_nonnegative_matrix_factor CONSTANT VARCHAR2(30) := 'ALGO_NONNEGATIVE_MATRIX_FACTOR';
algo_apriori_association_rules CONSTANT VARCHAR2(30) := 'ALGO_APRIORI_ASSOCIATION_RULES';
algo_kmeans CONSTANT VARCHAR2(30) := 'ALGO_KMEANS';
algo_ocluster CONSTANT VARCHAR2(30) := 'ALGO_O_CLUSTER'
algo_ai_mdl CONSTANT VARCHAR2(30) := 'ALGO_AI_MDL';
algo_decision_tree CONSTANT VARCHAR2(30) := 'ALGO_DECISION_TREE';
algo_random_forest CONSTANT VARCHAR2(30) := 'ALGO_RANDOM_FOREST';
algo_generalized_linear_model CONSTANT VARCHAR2(30) := 'ALGO_GENERALIZED_LINEAR_MODEL';
algo_singular_value_decomp CONSTANT VARCHAR2(30) := 'ALGO_SINGULAR_VALUE_DECOMP'
algo_expectation_maximization CONSTANT VARCHAR2(30) := 'ALGO_EXPECTATION_MAXIMIZATION';
algo_explicit_semantic_analys CONSTANT VARCHAR2(30) := 'ALGO_EXPLICIT_SEMANTIC_ANALYS';
algo_neural_network CONSTANT VARCHAR2(30) := 'ALGO_NEURAL_NETWORK';
algo_cur_decomposition CONSTANT VARCHAR2(30) := 'ALGO_CUR_DECOMPOSITION';
algo_exponential_smoothing CONSTANT VARCHAR2(30) := 'ALGO_EXPONENTIAL_SMOOTHING';
algo_mset_sprt CONSTANT VARCHAR2(30) := 'ALGO_MSET_SPRT'
algo_xgboost CONSTANT VARCHAR2(30) := 'ALGO_XGBOOST';

-- ALGORITHM SETTINGS AND VALUES
--
-- ABN: Setting Names
abns_model_type CONSTANT VARCHAR2(30) := 'ABNS_MODEL_TYPE';
abns_max_build_minutes CONSTANT VARCHAR2(30) := 'ABNS_MAX_BUILD_MINUTES';
abns_max_predictors CONSTANT VARCHAR2(30) := 'ABNS_MAX_PREDICTORS';
abns_max_nb_predictors CONSTANT VARCHAR2(30) := 'ABNS_MAX_NB_PREDICTORS';

-- ABN: Setting Values for abns_model_type
abns_multi_feature CONSTANT VARCHAR2(30) := 'ABNS_MULTI_FEATURE';
abns_single_feature CONSTANT VARCHAR2(30) := 'ABNS_SINGLE_FEATURE';
abns_naive_bayes CONSTANT VARCHAR2(30) := 'ABNS_NAIVE_BAYES';

-- NB: Setting Names
nabs_pairwise_threshold CONSTANT VARCHAR2(30) := 'NABS_PAIRWISE_THRESHOLD';
nabs_singleton_threshold CONSTANT VARCHAR2(30) := 'NABS_SINGLETON_THRESHOLD';

-- SVM: Setting Names
-- NOTE: svms_epsilon applies only for SVM Regression
-- svms_complexity_factor applies to both
-- svms_std_dev applies only for Gaussian Kernels
-- kernel_cache_size to Gaussian kernels only
svms_conv_tolerance CONSTANT VARCHAR2(30) := 'SVMS_CONV_TOLERANCE';
svms_std_dev CONSTANT VARCHAR2(30) := 'SVMS_STD_DEV';
svms_complexity_factor CONSTANT VARCHAR2(30) := 'SVMS_COMPLEXITY_FACTOR';
svms_kernel_cache_size CONSTANT VARCHAR2(30) := 'SVMS_KERNEL_CACHE_SIZE';
svms_epsilon CONSTANT VARCHAR2(30) := 'SVMS_EPSILON';
svms_kernel_function CONSTANT VARCHAR2(30) := 'SVMS_KERNEL_FUNCTION';
svms_active_learning CONSTANT VARCHAR2(30) := 'SVMS_ACTIVE_LEARNING';
svms_outlier_rate CONSTANT VARCHAR2(30) := 'SVMS_OUTLIER_RATE';
svms_num_iterations CONSTANT VARCHAR2(30) := 'SVMS_NUM_ITERATIONS';
svms_num_pivots CONSTANT VARCHAR2(30) := 'SVMS_NUM_PIVOTS';
svms_batch_rows CONSTANT VARCHAR2(30) := 'SVMS_BATCH_ROWS';
svms_regularizer CONSTANT VARCHAR2(30) := 'SVMS_REGULARIZER';
svms_solver CONSTANT VARCHAR2(30) := 'SVMS_SOLVER';

-- SVM: Setting Values for svms_kernel_function
svms_linear CONSTANT VARCHAR2(30) := 'SVMS_LINEAR';
svms_gaussian CONSTANT VARCHAR2(30) := 'SVMS_GAUSSIAN';

-- SVM: Setting Values for svms_active_learning
svms_al_enable CONSTANT VARCHAR2(30) := 'SVMS_AL_ENABLE';
svms_al_disable CONSTANT VARCHAR2(30) := 'SVMS_AL_DISABLE';

-- SVM: Setting Values for svms_regularizer
svms_regularizer_l1 CONSTANT VARCHAR2(30) := 'SVMS_REGULARIZER_L1';
svms_regularizer_l2 CONSTANT VARCHAR2(30) := 'SVMS_REGULARIZER_L2';

-- SVM: Setting Values for svms_solver
svms_solver_sgd CONSTANT VARCHAR2(30) := 'SVMS_SOLVER_SGD';
svms_solver_ipm CONSTANT VARCHAR2(30) := 'SVMS_SOLVER_IPM';

-- KMNS: Setting Names
kmns_distance CONSTANT VARCHAR2(30) := 'KMNS_DISTANCE';
kmns_iterations CONSTANT VARCHAR2(30) := 'KMNS_ITERATIONS';
kmns_conv_tolerance CONSTANT VARCHAR2(30) := 'KMNS_CONV_TOLERANCE';
kmns_split_criterion CONSTANT VARCHAR2(30) := 'KMNS_SPLIT_CRITERION';
kmns_min_pct_attr_support CONSTANT VARCHAR2(30):= 'KMNS_MIN_PCT_ATTR_SUPPORT';
kmns_block_growth CONSTANT VARCHAR2(30) := 'KMNS_BLOCK_GROWTH';
kmns_num_bins CONSTANT VARCHAR2(30) := 'KMNS_NUM_BINS';
kmns_details CONSTANT VARCHAR2(30) := 'KMNS_DETAILS';
kmns_random_seed CONSTANT VARCHAR2(30) := 'KMNS_RANDOM_SEED';

-- KMNS: Setting Values for kmns_distance
kmns_euclidean CONSTANT VARCHAR2(30) := 'KMNS_EUCLIDEAN';
kmns_cosine CONSTANT VARCHAR2(30) := 'KMNS_COSINE';
kmns_fast_cosine CONSTANT VARCHAR2(30) := 'KMNS_FAST_COSINE';

-- KMNS: Setting Values for kmns_split_criterion
kmns_size CONSTANT VARCHAR2(30) := 'KMNS_SIZE'
kmns_variance CONSTANT VARCHAR2(30) := 'KMNS_VARIANCE';

-- KMNS: Setting Values for kmns_details
kmns_details_none CONSTANT VARCHAR2(30) := 'KMNS_DETAILS_NONE';
kmns_details_hierarchy CONSTANT VARCHAR2(30) := 'KMNS_DETAILS_HIERARCHY';
kmns_details_all CONSTANT VARCHAR2(30) := 'KMNS_DETAILS_ALL';

-- NMF: Setting Names
nmfs_num_iterations CONSTANT VARCHAR2(30) := 'NMFS_NUM_ITERATIONS';
nmfs_conv_tolerance CONSTANT VARCHAR2(30) := 'NMFS_CONV_TOLERANCE';
nmfs_random_seed CONSTANT VARCHAR2(30) := 'NMFS_RANDOM_SEED'
nmfs_nonnegative_scoring CONSTANT VARCHAR2(30) := 'NMFS_NONNEGATIVE_SCORING';
-- Setting values for NMFS_NONNEGATIVE_SCORING
nmfs_nonneg_scoring_enable CONSTANT VARCHAR2(30) := 'NMFS_NONNEG_SCORING_ENABLE';
nmfs_nonneg_scoring_disable CONSTANT VARCHAR2(30) := 'NMFS_NONNEG_SCORING_DISABLE';

-- OCLT: Setting Names for O-Cluster
oclt_sensitivity CONSTANT VARCHAR2(30) := 'OCLT_SENSITIVITY';
oclt_max_buffer CONSTANT VARCHAR2(30) := 'OCLT_MAX_BUFFER';

-- TREE: Setting Names
tree_impurity_metric CONSTANT VARCHAR2(30) := 'TREE_IMPURITY_METRIC';
tree_term_max_depth CONSTANT VARCHAR2(30) := 'TREE_TERM_MAX_DEPTH';
tree_term_minrec_split CONSTANT VARCHAR2(30) := 'TREE_TERM_MINREC_SPLIT';
tree_term_minpct_split CONSTANT VARCHAR2(30) := 'TREE_TERM_MINPCT_SPLIT';
tree_term_minrec_node CONSTANT VARCHAR2(30) := 'TREE_TERM_MINREC_NODE';
tree_term_minpct_node CONSTANT VARCHAR2(30) := 'TREE_TERM_MINPCT_NODE';

-- TREE: Setting Values for tree_impurity_metric
tree_impurity_gini CONSTANT VARCHAR2(30) := 'TREE_IMPURITY_GINI';
tree_impurity_entropy CONSTANT VARCHAR2(30) := 'TREE_IMPURITY_ENTROPY';

-- RANDOM FOREST: Setting Names
rfor_mtry CONSTANT VARCHAR2(30) := 'RFOR_MTRY';
rfor_num_trees CONSTANT VARCHAR2(30) := 'RFOR_NUM_TREES';
rfor_sampling_ratio CONSTANT VARCHAR2(30) := 'RFOR_SAMPLING_RATIO';

-- GLM: Setting Names
glms_ridge_regression CONSTANT VARCHAR2(30) := 'GLMS_RIDGE_REGRESSION';
glms_row_diagnostics CONSTANT VARCHAR2(30) := 'GLMS_ROW_DIAGNOSTICS';
glms_diagnostics_table_name CONSTANT VARCHAR2(30) := 'GLMS_DIAGNOSTICS_TABLE_NAME';
glms_reference_class_name CONSTANT VARCHAR2(30) := 'GLMS_REFERENCE_CLASS_NAME';
glms_ridge_value CONSTANT VARCHAR2(30) := 'GLMS_RIDGE_VALUE';
glms_conf_level CONSTANT VARCHAR2(30) := 'GLMS_CONF_LEVEL';
glms_vif_for_ridge CONSTANT VARCHAR2(30) := 'GLMS_VIF_FOR_RIDGE';
glms_solver CONSTANT VARCHAR2(30) := 'GLMS_SOLVER';
glms_sparse_solver CONSTANT VARCHAR2(30) := 'GLMS_SPARSE_SOLVER';

-- GLM: Setting Values for glms_ridge_regression
glms_ridge_reg_enable CONSTANT VARCHAR2(30) := 'GLMS_RIDGE_REG_ENABLE';
glms_ridge_reg_disable CONSTANT VARCHAR2(30) := 'GLMS_RIDGE_REG_DISABLE';

-- GLM: Setting Values for glms_row_diagnostics
glms_row_diag_enable CONSTANT VARCHAR2(30) := 'GLMS_ROW_DIAG_ENABLE';
glms_row_diag_disable CONSTANT VARCHAR2(30) := 'GLMS_ROW_DIAG_DISABLE';

-- GLM: Setting Values for glms_vif_for_ridge
glms_vif_ridge_enable CONSTANT VARCHAR2(30) := 'GLMS_VIF_RIDGE_ENABLE';
glms_vif_ridge_disable CONSTANT VARCHAR2(30) := 'GLMS_VIF_RIDGE_DISABLE';

-- GLM: Setting Values for glms_ftr_selection
glms_ftr_selection CONSTANT VARCHAR2(30) := 'GLMS_FTR_SELECTION';
glms_ftr_selection_enable CONSTANT VARCHAR2(30) := 'GLMS_FTR_SELECTION_ENABLE';
glms_ftr_selection_disable CONSTANT VARCHAR2(30) := 'GLMS_FTR_SELECTION_DISABLE';

-- GLM: Setting Values for glms_ftr_sel_crit
glms_ftr_sel_crit CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_CRIT';
glms_ftr_sel_aic CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_AIC';
glms_ftr_sel_sbic CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_SBIC';
glms_ftr_sel_ric CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_RIC';
glms_ftr_sel_alpha_inv CONSTANT VARCHAR2(30) := 'GLMS_FTR_SEL_ALPHA_INV';

-- GLM: Setting Values for glms_feature_generation
glms_ftr_generation CONSTANT VARCHAR2(30) := 'GLMS_FTR_GENERATION';
glms_ftr_generation_enable CONSTANT VARCHAR2(30) := 'GLMS_FTR_GENERATION_ENABLE';
glms_ftr_generation_disable CONSTANT VARCHAR2(30) := 'GLMS_FTR_GENERATION_DISABLE'

-- GLM: Setting Values for glms_feature_gen
glms_ftr_gen_method CONSTANT VARCHAR2(30) := 'GLMS_FTR_GEN_METHOD';
glms_ftr_gen_quadratic CONSTANT VARCHAR2(30) := 'GLMS_FTR_GEN_QUADRATIC';
glms_ftr_gen_cubic CONSTANT VARCHAR2(30) := 'GLMS_FTR_GEN_CUBIC';

-- GLM: feature selection categorical value handling
glms_select_block CONSTANT VARCHAR2(30) := 'GLMS_SELECT_BLOCK';
glms_select_block_disable CONSTANT VARCHAR2(30) := 'GLMS_SELECT_BLOCK_DISABLE';
glms_select_block_enable CONSTANT VARCHAR2(30) := 'GLMS_SELECT_BLOCK_ENABLE';

-- GLM: feature selection - max features selected
glms_max_features CONSTANT VARCHAR2(30) := 'GLMS_MAX_FEATURES';

-- GLM: feature identification - whether row sampling is used in the selection of feature
glms_ftr_identification CONSTANT VARCHAR2(30) := 'GLMS_FTR_IDENTIFICATION';
glms_ftr_ident_quick CONSTANT VARCHAR2(30) := 'GLMS_FTR_IDENT_QUICK';
glms_ftr_ident_complete CONSTANT VARCHAR2(30) := 'GLMS_FTR_IDENT_COMPLETE';

-- GLM: model pruning-whether the final model features will be pruned using t-statistics
glms_prune_model CONSTANT VARCHAR2(30) := 'GLMS_PRUNE_MODEL';
glms_prune_model_enable CONSTANT VARCHAR2(30) := 'GLMS_PRUNE_MODEL_ENABLE';
glms_prune_model_disable CONSTANT VARCHAR2(30) := 'GLMS_PRUNE_MODEL_DISABLE';

-- GLM: feature acceptance - whether partitioning the data into feature
-- ordering and feature selection sets will be used (strict) or not (relaxed
glms_ftr_acceptance CONSTANT VARCHAR2(30) := 'GLMS_FTR_ACCEPTANCE'
glms_ftr_acceptance_strict CONSTANT VARCHAR2(30) := 'GLMS_FTR_ACCEPTANCE_STRICT';
glms_ftr_acceptance_relaxed CONSTANT VARCHAR2(30) := 'GLMS_FTR_ACCEPTANCE_RELAXED';

-- GLM: convergence tolerance
glms_conv_tolerance CONSTANT VARCHAR2(30) := 'GLMS_CONV_TOLERANCE';
-- GLM: number of iterations
glms_num_iterations CONSTANT VARCHAR2(30) := 'GLMS_NUM_ITERATIONS';
-- GLM: number of rows in a batch
glms_batch_rows CONSTANT VARCHAR2(30) := 'GLMS_BATCH_ROWS';

-- GLM: Setting Values for glms_solver
glms_solver_sgd CONSTANT VARCHAR2(30) := 'GLMS_SOLVER_SGD';
glms_solver_chol CONSTANT VARCHAR2(30) := 'GLMS_SOLVER_CHOL';
glms_solver_qr CONSTANT VARCHAR2(30) := 'GLMS_SOLVER_QR';
glms_solver_lbfgs_admm CONSTANT VARCHAR2(30) := 'GLMS_SOLVER_LBFGS_ADMM';

-- GLM: Setting Values for glms_sparse_solver
glms_sparse_solver_enable CONSTANT VARCHAR2(30) := 'GLMS_SPARSE_SOLVER_ENABLE';
glms_sparse_solver_disable CONSTANT VARCHAR2(30) := 'GLMS_SPARSE_SOLVER_DISABLE';

-- SVD max number of features allowed
svds_max_num_features CONSTANT NUMBER := 2500;

svds_scoring_mode CONSTANT VARCHAR2(30) := 'SVDS_SCORING_MODE';
-- SVD: Setting values for svds_scoring_mode
svds_scoring_svd CONSTANT VARCHAR2(30) := 'SVDS_SCORING_SVD';
svds_scoring_pca CONSTANT VARCHAR2(30) := 'SVDS_SCORING_PCA';
svds_u_matrix_output CONSTANT VARCHAR2(30) := 'SVDS_U_MATRIX_OUTPUT';
-- SVD: Setting values for svds_u_matrix_output
svds_u_matrix_enable CONSTANT VARCHAR2(30) := 'SVDS_U_MATRIX_ENABLE';
svds_u_matrix_disable CONSTANT VARCHAR2(30) := 'SVDS_U_MATRIX_DISABLE';
-- SVD: tolerance
svds_tolerance CONSTANT VARCHAR2(30) := 'SVDS_TOLERANCE';
-- SVD: Random seed
svds_random_seed CONSTANT VARCHAR2(30) := 'SVDS_RANDOM_SEED'
-- SVD: Oversampling
svds_over_sampling CONSTANT VARCHAR2(30) := 'SVDS_OVER_SAMPLING'
-- SVD: Power iterations
svds_power_iterations CONSTANT VARCHAR2(30) := 'SVDS_POWER_ITERATIONS';
-- SVD: Solver
svds_solver CONSTANT VARCHAR2(30) := 'SVDS_SOLVER'
svds_solver_data_driven CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_DATA_DRIVEN';
svds_solver_tssvd CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_TSSVD'
svds_solver_ssvd CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_SSVD'
svds_solver_tseigen CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_TSEIGEN'
svds_solver_steigen CONSTANT VARCHAR2(30) := 'SVDS_SOLVER_STEIGEN'

-- EM number of components
emcs_num_components CONSTANT VARCHAR2(30) := 'EMCS_NUM_COMPONENTS';

-- high-level component clustering
emcs_cluster_components CONSTANT VARCHAR2(30) := 'EMCS_CLUSTER_COMPONENTS';
-- values for emcs_cluster_components
emcs_cluster_comp_enable CONSTANT VARCHAR2(30) := 'EMCS_CLUSTER_COMP_ENABLE';
emcs_cluster_comp_disable CONSTANT VARCHAR2(30) := 'EMCS_CLUSTER_COMP_DISABLE';

-- high-level cluster threshold
emcs_cluster_thresh CONSTANT VARCHAR2(30) := 'EMCS_CLUSTER_THRESH';

-- max number of 2D attributes
emcs_max_num_attr_2d CONSTANT VARCHAR2(30) := 'EMCS_MAX_NUM_ATTR_2D';

-- number of projections
emcs_num_projections CONSTANT VARCHAR2(30) := 'EMCS_NUM_PROJECTIONS';

-- number of quantile bins
emcs_num_quantile_bins CONSTANT VARCHAR2(30) := 'EMCS_NUM_QUANTILE_BINS';

-- number of topN bins
emcs_num_topn_bins CONSTANT VARCHAR2(30) := 'EMCS_NUM_TOPN_BINS';

-- number of equi-width bins
emcs_num_equiwidth_bins CONSTANT VARCHAR2(30) := 'EMCS_NUM_EQUIWIDTH_BINS';

-- minimum percentage attribute support
emcs_min_pct_attr_support CONSTANT VARCHAR2(30) := 'EMCS_MIN_PCT_ATTR_SUPPORT';
-- full covariance (next release)
-- emcs_full_covariance CONSTANT VARCHAR2(30) := 'EMCS_FULL_COVARIANCE';
-- values for emcs_full_covariance
-- emcs_full_cov_enable CONSTANT VARCHAR2(30) := 'EMCS_FULL_COV_ENABLE';
-- emcs_full_cov_disable CONSTANT VARCHAR2(30) := 'EMCS_FULL_COV_DISABLE';

-- cluster statistics
emcs_cluster_statistics CONSTANT VARCHAR2(30) := 'EMCS_CLUSTER_STATISTICS';
-- values for emcs_cluster_statistics
emcs_clus_stats_enable CONSTANT VARCHAR2(30) := 'EMCS_CLUS_STATS_ENABLE';
emcs_clus_stats_disable CONSTANT VARCHAR2(30) := 'EMCS_CLUS_STATS_DISABLE';

-- distribution for modeling numerical attributes
emcs_num_distribution CONSTANT VARCHAR2(30) := 'EMCS_NUM_DISTRIBUTION';
-- values for emcs_num_distribution
emcs_num_distr_bernoulli CONSTANT VARCHAR2(30) := 'EMCS_NUM_DISTR_BERNOULLI';
emcs_num_distr_gaussian CONSTANT VARCHAR2(30) := 'EMCS_NUM_DISTR_GAUSSIAN';
emcs_num_distr_system CONSTANT VARCHAR2(30) := 'EMCS_NUM_DISTR_SYSTEM';

-- number of iterations
emcs_num_iterations CONSTANT VARCHAR2(30) := 'EMCS_NUM_ITERATIONS';

-- required log likelihood improvement
emcs_loglike_improvement CONSTANT VARCHAR2(30) := 'EMCS_LOGLIKE_IMPROVEMENT';

-- linkage function
emcs_linkage_function CONSTANT VARCHAR2(30) := 'EMCS_LINKAGE_FUNCTION';
-- values for linkage function
emcs_linkage_single CONSTANT VARCHAR2(30) := 'EMCS_LINKAGE_SINGLE';
emcs_linkage_average CONSTANT VARCHAR2(30) := 'EMCS_LINKAGE_AVERAGE';
emcs_linkage_complete CONSTANT VARCHAR2(30) := 'EMCS_LINKAGE_COMPLETE';

-- attribute filtering
emcs_attribute_filter CONSTANT VARCHAR2(30) := 'EMCS_ATTRIBUTE_FILTER';
-- values for attribute filtering
emcs_attr_filter_enable CONSTANT VARCHAR2(30) := 'EMCS_ATTR_FILTER_ENABLE';
emcs_attr_filter_disable CONSTANT VARCHAR2(30) := 'EMCS_ATTR_FILTER_DISABLE';
-- convergence criterion
emcs_convergence_criterion CONSTANT VARCHAR2(30) := 'EMCS_CONVERGENCE_CRITERION';
-- values for convergence criterion
emcs_conv_crit_heldaside CONSTANT VARCHAR2(30) := 'EMCS_CONV_CRIT_HELDASIDE';
emcs_conv_crit_bic CONSTANT VARCHAR2(30) := 'EMCS_CONV_CRIT_BIC'
-- random seed
emcs_random_seed CONSTANT VARCHAR2(30) := 'EMCS_RANDOM_SEED';

-- model search
emcs_model_search CONSTANT VARCHAR2(30) := 'EMCS_MODEL_SEARCH';
-- values for model search
emcs_model_search_enable CONSTANT VARCHAR2(30) := 'EMCS_MODEL_SEARCH_ENABLE';
emcs_model_search_disable CONSTANT VARCHAR2(30) := 'EMCS_MODEL_SEARCH_DISABLE';

-- remove components
emcs_remove_components CONSTANT VARCHAR2(30) := 'EMCS_REMOVE_COMPONENTS';
-- values for remove components
emcs_remove_comps_enable CONSTANT VARCHAR2(30) := 'EMCS_REMOVE_COMPS_ENABLE';
emcs_remove_comps_disable CONSTANT VARCHAR2(30) := 'EMCS_REMOVE_COMPS_DISABLE';

-- ESA
esas_value_threshold CONSTANT VARCHAR2(30) := 'ESAS_VALUE_THRESHOLD';
esas_min_items CONSTANT VARCHAR2(30) := 'ESAS_MIN_ITEMS';
esas_topn_features CONSTANT VARCHAR2(30) := 'ESAS_TOPN_FEATURES';

-- ADMM
admm_iterations CONSTANT VARCHAR2(30) := 'ADMM_ITERATIONS';
admm_consensus CONSTANT VARCHAR2(30) := 'ADMM_CONSENSUS';
admm_tolerance CONSTANT VARCHAR2(30) := 'ADMM_TOLERANCE';

-- LBFGS
lbfgs_history_depth CONSTANT VARCHAR2(30) := 'LBFGS_HISTORY_DEPTH';
lbfgs_scale_hessian CONSTANT VARCHAR2(30) := 'LBFGS_SCALE_HESSIAN';
lbfgs_scale_hessian_enable CONSTANT VARCHAR2(30) := 'LBFGS_SCALE_HESSIAN_ENABLE';
lbfgs_scale_hessian_disable CONSTANT VARCHAR2(30) := 'LBFGS_SCALE_HESSIAN_DISABLE';
lbfgs_gradient_tolerance CONSTANT VARCHAR2(30) := 'LBFGS_GRADIENT_TOLERANCE';

-- RGLU: Setting Values
ralg_build_function CONSTANT VARCHAR2(30) := 'RALG_BUILD_FUNCTION';
ralg_build_parameter CONSTANT VARCHAR2(30) := 'RALG_BUILD_PARAMETER';
ralg_score_function CONSTANT VARCHAR2(30) := 'RALG_SCORE_FUNCTION';
ralg_details_function CONSTANT VARCHAR2(30) := 'RALG_DETAILS_FUNCTION';
ralg_details_format CONSTANT VARCHAR2(30) := 'RALG_DETAILS_FORMAT';
ralg_weight_function CONSTANT VARCHAR2(30) := 'RALG_WEIGHT_FUNCTION';
ralg_featurematrix_function CONSTANT VARCHAR2(30) := 'RALG_FEATUREMATRIX_FUNCTION';
ralg_clustercenter_function CONSTANT VARCHAR2(30) := 'RALG_CLUSTERCENTER_FUNCTION';
r_formula CONSTANT VARCHAR2(30) := 'R_FORMULA';

-- NNET
nnet_hidden_layers CONSTANT VARCHAR2(30) := 'NNET_HIDDEN_LAYERS';
nnet_nodes_per_layer CONSTANT VARCHAR2(30) := 'NNET_NODES_PER_LAYER';
nnet_iterations CONSTANT VARCHAR2(30) := 'NNET_ITERATIONS';
nnet_tolerance CONSTANT VARCHAR2(30) := 'NNET_TOLERANCE';
nnet_activations CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS';
nnet_activations_log_sig CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_LOG_SIG';
nnet_activations_linear CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_LINEAR';
nnet_activations_tanh CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_TANH';
nnet_activations_arctan CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_ARCTAN';
nnet_activations_bipolar_sig CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_BIPOLAR_SIG';
nnet_activations_relu CONSTANT VARCHAR2(30) := 'NNET_ACTIVATIONS_RELU';
nnet_regularizer CONSTANT VARCHAR2(30) := 'NNET_REGULARIZER';
nnet_regularizer_heldaside CONSTANT VARCHAR2(30) := 'NNET_REGULARIZER_HELDASIDE';
nnet_regularizer_l2 CONSTANT VARCHAR2(30) := 'NNET_REGULARIZER_L2';
nnet_regularizer_none CONSTANT VARCHAR2(30) := 'NNET_REGULARIZER_NONE';
nnet_heldaside_ratio CONSTANT VARCHAR2(30) := 'NNET_HELDASIDE_RATIO';
nnet_heldaside_max_fail CONSTANT VARCHAR2(30) := 'NNET_HELDASIDE_MAX_FAIL';
nnet_reg_lambda CONSTANT VARCHAR2(30) := 'NNET_REG_LAMBDA';
nnet_weight_lower_bound CONSTANT VARCHAR2(30) := 'NNET_WEIGHT_LOWER_BOUND';
nnet_weight_upper_bound CONSTANT VARCHAR2(30) := 'NNET_WEIGHT_UPPER_BOUND';
nnet_solver CONSTANT VARCHAR2(30) := 'NNET_SOLVER';
nnet_solver_adam CONSTANT VARCHAR2(30) := 'NNET_SOLVER_ADAM';
nnet_solver_lbfgs CONSTANT VARCHAR2(30) := 'NNET_SOLVER_LBFGS';

-- ADAM
adam_batch_rows CONSTANT VARCHAR2(30) := 'ADAM_BATCH_ROWS';
adam_alpha CONSTANT VARCHAR2(30) := 'ADAM_ALPHA';
adam_beta1 CONSTANT VARCHAR2(30) := 'ADAM_BETA1';
adam_beta2 CONSTANT VARCHAR2(30) := 'ADAM_BETA2';
adam_gradient_tolerance CONSTANT VARCHAR2(30) := 'ADAM_GRADIENT_TOLERANCE';

-- CUR approximated number of selected attributes
curs_approx_attr_num CONSTANT VARCHAR2(30) := 'CURS_APPROX_ATTR_NUM';

-- row importance
curs_row_importance CONSTANT VARCHAR2(30) := 'CURS_ROW_IMPORTANCE';

-- row importance values
curs_row_imp_enable CONSTANT VARCHAR2(30) := 'CURS_ROW_IMP_ENABLE';
curs_row_imp_disable CONSTANT VARCHAR2(30) := 'CURS_ROW_IMP_DISABLE';

-- approximated number of selected rows
curs_approx_row_num CONSTANT VARCHAR2(30) := 'CURS_APPROX_ROW_NUM';

-- SVD rank
curs_svd_rank CONSTANT VARCHAR2(30) := 'CURS_SVD_RANK';

-- EXSM
exsm_model CONSTANT VARCHAR2(30) := 'EXSM_MODEL';
exsm_simple CONSTANT VARCHAR2(30) := 'EXSM_SIMPLE';
exsm_simple_mult CONSTANT VARCHAR2(30) := 'EXSM_SIMPLE_MULT_ERR';
exsm_holt CONSTANT VARCHAR2(30) := 'EXSM_HOLT';
exsm_holt_dmp CONSTANT VARCHAR2(30) := 'EXSM_HOLT_DAMPED';
exsm_mul_trnd CONSTANT VARCHAR2(30) := 'EXSM_MULT_TREND';
exsm_multrd_dmp CONSTANT VARCHAR2(30) := 'EXSM_MULT_TREND_DAMPED';
exsm_seas_add CONSTANT VARCHAR2(30) := 'EXSM_SEASON_ADD';
exsm_seas_mul CONSTANT VARCHAR2(30) := 'EXSM_SEASON_MUL';
exsm_hw CONSTANT VARCHAR2(30) := 'EXSM_WINTERS';
exsm_hw_dmp CONSTANT VARCHAR2(30) := 'EXSM_WINTERS_DAMPED';
exsm_hw_addsea CONSTANT VARCHAR2(30) := 'EXSM_ADDWINTERS';
exsm_dhw_addsea CONSTANT VARCHAR2(30) := 'EXSM_ADDWINTERS_DAMPED';
exsm_hwmt CONSTANT VARCHAR2(30) := 'EXSM_WINTERS_MUL_TREND';
exsm_hwmt_dmp CONSTANT VARCHAR2(30) := 'EXSM_WINTERS_MUL_TREND_DMP';
exsm_seasonality CONSTANT VARCHAR2(30) := 'EXSM_SEASONALITY';
exsm_interval CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL';
exsm_interval_year CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_YEAR';
exsm_interval_qtr CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_QTR';
exsm_interval_month CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_MONTH';
exsm_interval_week CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_WEEK';
exsm_interval_day CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_DAY';
exsm_interval_hour CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_HOUR';
exsm_interval_min CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_MINUTE';
exsm_interval_sec CONSTANT VARCHAR2(30) := 'EXSM_INTERVAL_SECOND';
exsm_accumulate CONSTANT VARCHAR2(30) := 'EXSM_ACCUMULATE';
exsm_accu_total CONSTANT VARCHAR2(30) := 'EXSM_ACCU_TOTAL';
exsm_accu_std CONSTANT VARCHAR2(30) := 'EXSM_ACCU_STD';
exsm_accu_max CONSTANT VARCHAR2(30) := 'EXSM_ACCU_MAX';
exsm_accu_min CONSTANT VARCHAR2(30) := 'EXSM_ACCU_MIN';
exsm_accu_avg CONSTANT VARCHAR2(30) := 'EXSM_ACCU_AVG';
exsm_accu_median CONSTANT VARCHAR2(30) := 'EXSM_ACCU_MEDIAN';
exsm_accu_count CONSTANT VARCHAR2(30) := 'EXSM_ACCU_COUNT';
exsm_setmissing CONSTANT VARCHAR2(30) := 'EXSM_SETMISSING';
exsm_miss_min CONSTANT VARCHAR2(30) := 'EXSM_MISS_MIN';
exsm_miss_max CONSTANT VARCHAR2(30) := 'EXSM_MISS_MAX';
exsm_miss_avg CONSTANT VARCHAR2(30) := 'EXSM_MISS_AVG';
exsm_miss_median CONSTANT VARCHAR2(30) := 'EXSM_MISS_MEDIAN';
exsm_miss_last CONSTANT VARCHAR2(30) := 'EXSM_MISS_LAST';
exsm_miss_first CONSTANT VARCHAR2(30) := 'EXSM_MISS_FIRST';
exsm_miss_prev CONSTANT VARCHAR2(30) := 'EXSM_MISS_PREV';
exsm_miss_next CONSTANT VARCHAR2(30) := 'EXSM_MISS_NEXT';
exsm_miss_auto CONSTANT VARCHAR2(30) := 'EXSM_MISS_AUTO';
exsm_prediction_step CONSTANT VARCHAR2(30) := 'EXSM_PREDICTION_STEP';
exsm_opt_criterion CONSTANT VARCHAR2(30) := 'EXSM_OPTIMIZATION_CRIT';
exsm_opt_crit_lik CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_LIK';
exsm_opt_crit_mse CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_MSE';
exsm_opt_crit_amse CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_AMSE';
exsm_opt_crit_sig CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_SIG';
exsm_opt_crit_mae CONSTANT VARCHAR2(30) := 'EXSM_OPT_CRIT_MAE';
exsm_nmse CONSTANT VARCHAR2(30) := 'EXSM_NMSE';
exsm_confidence_level CONSTANT VARCHAR2(30) := 'EXSM_CONFIDENCE_LEVEL';


--MSET-SPRT
mset_memory_vectors CONSTANT VARCHAR2(30) := 'MSET_MEMORY_VECTORS'
mset_adb_height CONSTANT VARCHAR2(30) := 'MSET_ADB_HEIGHT'
mset_std_tolerance CONSTANT VARCHAR2(30) := 'MSET_STD_TOLERANCE'
mset_alpha_prob CONSTANT VARCHAR2(30) := 'MSET_ALPHA_PROB';
mset_beta_prob CONSTANT VARCHAR2(30) := 'MSET_BETA_PROB';
mset_alert_count CONSTANT VARCHAR2(30) := 'MSET_ALERT_COUNT';
mset_alert_window CONSTANT VARCHAR2(30) := 'MSET_ALERT_WINDOW';
mset_heldaside CONSTANT VARCHAR2(30) := 'MSET_HELDASIDE';
mset_projection_threshold CONSTANT VARCHAR2(30) := 'MSET_PROJECTION_THRESHOLD';

-- XGBoost
xgboost_num_round CONSTANT VARCHAR2(30) := 'num_round';
xgboost_booster CONSTANT VARCHAR2(30) := 'booster';
xgboost_objective CONSTANT VARCHAR2(30) := 'objective';
xgboost_eta CONSTANT VARCHAR2(30) := 'eta';
xgboost_gamma CONSTANT VARCHAR2(30) := 'gamma';
xgboost_max_depth CONSTANT VARCHAR2(30) := 'max_depth';
xgboost_min_child_weight CONSTANT VARCHAR2(30) := 'min_child_weight';
xgboost_max_delta_step CONSTANT VARCHAR2(30) := 'max_delta_step';
xgboost_subsample CONSTANT VARCHAR2(30) := 'subsample';
xgboost_colsample_bytree CONSTANT VARCHAR2(30) := 'colsample_bytree';
xgboost_colsample_bylevel CONSTANT VARCHAR2(30) := 'colsample_bylevel';
xgboost_lambda CONSTANT VARCHAR2(30) := 'lambda';
xgboost_alpha CONSTANT VARCHAR2(30) := 'alpha';
xgboost_tree_method CONSTANT VARCHAR2(30) := 'tree_method';
xgboost_sketch_eps CONSTANT VARCHAR2(30) := 'sketch_eps';
xgboost_scale_pos_weight CONSTANT VARCHAR2(30) := 'scale_pos_weight';
xgboost_updater CONSTANT VARCHAR2(30) := 'updater';
xgboost_grow_policy CONSTANT VARCHAR2(30) := 'grow_policy';
xgboost_max_leaves CONSTANT VARCHAR2(30) := 'max_leaves';
xgboost_max_bin CONSTANT VARCHAR2(30) := 'max_bin';
xgboost_predictor CONSTANT VARCHAR2(30) := 'predictor';
xgboost_sample_type CONSTANT VARCHAR2(30) := 'sample_type';
xgboost_normalize_type CONSTANT VARCHAR2(30) := 'normalize_type';
xgboost_rate_drop CONSTANT VARCHAR2(30) := 'rate_drop';
xgboost_one_drop CONSTANT VARCHAR2(30) := 'one_drop';
xgboost_skip_drop CONSTANT VARCHAR2(30) := 'skip_drop';
xgboost_tweedie_variance_power CONSTANT VARCHAR2(30) := 'tweedie_variance_power';
xgboost_base_score CONSTANT VARCHAR2(30) := 'base_score';
xgboost_eval_metric CONSTANT VARCHAR2(30) := 'eval_metric';
xgboost_seed CONSTANT VARCHAR2(30) := 'seed';
xgboost_ntree_limit CONSTANT VARCHAR2(30) := 'ntree_limit';
xgboost_top_k CONSTANT VARCHAR2(30) := 'top_k';
xgboost_feature_selector CONSTANT VARCHAR2(30) := 'feature_selector';
xgboost_colsample_bynode CONSTANT VARCHAR2(30) := 'colsample_bynode';
xgboost_num_parallel_tree CONSTANT VARCHAR2(30) := 'num_parallel_tree';
Data Types TYPE SETTING_LIST IS TABLE OF CLOB INDEX BY VARCHAR2(30);

SUBTYPE TRANSFORM_LIST IS dbms_data_mining_transform.TRANSFORM_LIST;
Dependencies SELECT name FROM dba_dependencies WHERE referenced_name = 'DBMS_DATA_MINING' UNION
SELECT referenced_name FROM dba_dependencies WHERE name = 'DBMS_DATA_MINING'
ORDER BY 1;

Returns 67 objects
Documented No
First Available Not known but the creation date is January 11, 2002
Pragmas PRAGMA SUPPLEMENTAL_LOG_DATA(default, UNSUPPORTED);
Security Model Owned by SYS with EXECUTE granted to PUBLIC
Source {ORACLE_HOME}/rdbms/admin/dbmsdm.sql
{ORACLE_HOME}/rdbms/admin/prvtdm.plb
Subprograms
ADD_COST_MATRIX EXPORT_SERMODEL GET_MODEL_DETAILS_RA
ADD_PARTITION FETCH_ALG_SCHEMA GET_MODEL_DETAILS_SVD
ALTER_REVERSE_EXPRESSION GET_ASSOCIATION_RULES GET_MODEL_DETAILS_SVM
APPLY GET_DEFAULT_SETTINGS GET_MODEL_DETAILS_XML
COMPUTE_CONFUSION_MATRIX GET_FREQUENT_ITEMSETS GET_MODEL_R_FUNCTION
COMPUTE_CONFUSION_MATRIX_PART GET_MODEL_COST_MATRIX GET_MODEL_SETTINGS
COMPUTE_LIFT GET_MODEL_DETAILS_AI GET_MODEL_SIGNATURE
COMPUTE_LIFT_PART GET_MODEL_DETAILS_EM GET_MODEL_TRANSFORMATIONS
COMPUTE_ROC GET_MODEL_DETAILS_EM_COMP GET_TRANSFORM_LIST
COMPUTE_ROC_PART GET_MODEL_DETAILS_EM_PROJ IMPORT_MODEL
CREATE_MODEL GET_MODEL_DETAILS_GLM IMPORT_ONNX_MODEL
CREATE_MODEL2 GET_MODEL_DETAILS_GLOBAL IMPORT_SERMODEL
DROP_ALGORITHM GET_MODEL_DETAILS_KM RANK_APPLY
DROP_MODEL GET_MODEL_DETAILS_NB REGISTER_ALGORITHM
DROP_PARTITION GET_MODEL_DETAILS_NMF REMOVE_COST_MATRIX
EXPORT_MODEL GET_MODEL_DETAILS_OC RENAME_MODEL
 
ADD_COST_MATRIX
Undocumented dbms_data_mining.add_cost_matrix(
model_name              IN VARCHAR2,
cost_matrix_table_name  IN VARCHAR2,
cost_matrix_schema_name IN VARCHAR2 DEFAULT NULL,
partition_name          IN VARCHAR2 DEFAULT NULL);
TBD
 
ADD_PARTITION
Undocumented dbms_data_mining.add_partition(
model_name  IN VARCHAR2,
data_query  IN CLOB,
add_options IN VARCHAR2 DEFAULT 'ERROR');
TBD
 
ALTER_REVERSE_EXPRESSION
Undocumented dbms_data_mining.alter_reverse_expression(
model_name        IN VARCHAR2,
expression        IN CLOB,
attribute_name    IN VARCHAR2 DEFAULT NULL,
attribute_subname IN VARCHAR2 DEFAULT NULL);
TBD
 
APPLY
Undocumented dbms_data_mining.apply(
model_name          IN VARCHAR2,
data_table_name     IN VARCHAR2,
case_id_column_name IN VARCHAR2,
result_table_name   IN VARCHAR2,
data_schema_name    IN VARCHAR2 DEFAULT NULL);
TBD
 
COMPUTE_CONFUSION_MATRIX
Undocumented dbms_data_mining.compute_confusion_matrix(
accuracy                    OUT NUMBER,
apply_result_table_name     IN  VARCHAR2,
target_table_name           IN  VARCHAR2,
case_id_column_name         IN  VARCHAR2,
target_column_name          IN  VARCHAR2,
confusion_matrix_table_name IN  VARCHAR2,
score_column_name           IN  VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN  VARCHAR2 DEFAULT 'PROBABILITY',
cost_matrix_table_name      IN  VARCHAR2 DEFAULT NULL,
apply_result_schema_name    IN  VARCHAR2 DEFAULT NULL,
target_schema_name          IN  VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name     IN  VARCHAR2 DEFAULT NULL,
score_criterion_type        IN  VARCHAR2 DEFAULT NULL);
TBD
 
COMPUTE_CONFUSION_MATRIX_PART
Undocumented dbms_data_mining.compute_confusion_matrix_part(
accuracy                    OUT sys.dm_nested_numericals,
apply_result_table_name     IN  VARCHAR2,
target_table_name           IN  VARCHAR2,
case_id_column_name         IN  VARCHAR2,
target_column_name          IN  VARCHAR2,
confusion_matrix_table_name IN  VARCHAR2,
score_column_name           IN  VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN  VARCHAR2 DEFAULT 'PROBABILITY',
score_partition_column_name IN  VARCHAR2 DEFAULT 'PARTITION_NAME',
cost_matrix_table_name      IN  VARCHAR2 DEFAULT NULL,
apply_result_schema_name    IN  VARCHAR2 DEFAULT NULL,
target_schema_name          IN  VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name     IN  VARCHAR2 DEFAULT NULL,
score_criterion_type        IN  VARCHAR2);
TBD
 
COMPUTE_LIFT
Undocumented dbms_data_mining.compute_lift(
apply_result_table_name     IN VARCHAR2,
target_table_name           IN VARCHAR2,
case_id_column_name         IN VARCHAR2,
target_column_name          IN VARCHAR2,
lift_table_name             IN VARCHAR2,
positive_target_value       IN VARCHAR2,
score_column_name           IN VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN VARCHAR2 DEFAULT 'PROBABILITY',
num_quantiles               IN NUMBER   DEFAULT 10,
cost_matrix_table_name      IN VARCHAR2 DEFAULT NULL,
apply_result_schema_name    IN VARCHAR2 DEFAULT NULL,
target_schema_name          IN VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name     IN VARCHAR2 DEFAULT NULL,
score_criterion_type        IN VARCHAR2 DEFAULT NULL);
TBD
 
COMPUTE_LIFT_PART
Undocumented dbms_data_mining.compute_lift_part(
apply_result_table_name     IN VARCHAR2,
target_table_name           IN VARCHAR2,
case_id_column_name         IN VARCHAR2,
target_column_name          IN VARCHAR2,
lift_table_name             IN VARCHAR2,
positive_target_value       IN VARCHAR2,
score_column_name           IN VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN VARCHAR2 DEFAULT 'PROBABILITY',
score_partition_column_name IN VARCHAR2 DEFAULT 'PARTITION_NAME',
num_quantiles               IN NUMBER   DEFAULT 10,
cost_matrix_table_name      IN VARCHAR2 DEFAULT NULL,
apply_result_schema_name    IN VARCHAR2 DEFAULT NULL,
target_schema_name          IN VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name     IN VARCHAR2 DEFAULT NULL,
score_criterion_type        IN VARCHAR2 DEFAULT NULL);
TBD
 
COMPUTE_ROC
Undocumented dbms_data_mining.compute_roc(
roc_area_under_curve        OUT NUMBER,
apply_result_table_name     IN  VARCHAR2,
target_table_name           IN  VARCHAR2,
case_id_column_name         IN  VARCHAR2,
target_column_name          IN  VARCHAR2,
roc_table_name              IN  VARCHAR2,
positive_target_value       IN  VARCHAR2,
score_column_name           IN  VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN  VARCHAR2 DEFAULT 'PROBABILITY',
apply_result_schema_name    IN  VARCHAR2 DEFAULT NULL,
target_schema_name          IN  VARCHAR2 DEFAULT NULL);
TBD
 
COMPUTE_ROC_PART
Undocumented dbms_data_mining.compute_roc_part(
roc_area_under_curve        OUT sys.dm_nested_numericals,
apply_result_table_name     IN  VARCHAR2,
target_table_name           IN  VARCHAR2,
case_id_column_name         IN  VARCHAR2,
target_column_name          IN  VARCHAR2,
roc_table_name              IN  VARCHAR2,
positive_target_value       IN  VARCHAR2,
score_column_name           IN  VARCHAR2 DEFAULT 'PREDICTION',
score_criterion_column_name IN  VARCHAR2 DEFAULT 'PROBABILITY',
score_partition_column_name IN  VARCHAR2 DEFAULT 'PARTITION_NAME',
apply_result_schema_name    IN  VARCHAR2 DEFAULT NULL,
target_schema_name          IN  VARCHAR2 DEFAULT NULL);
TBD
 
CREATE_MODEL
Undocumented dbms_data_mining.create_model(
model_name           IN VARCHAR2,
mining_function      IN VARCHAR2,
data_table_name      IN VARCHAR2,
case_id_column_name  IN VARCHAR2,
target_column_name   IN VARCHAR2 DEFAULT NULL,
settings_table_name  IN VARCHAR2 DEFAULT NULL,
data_schema_name     IN VARCHAR2 DEFAULT NULL,
settings_schema_name IN VARCHAR2 DEFAULT NULL,
xform_list           IN sys.dbms_data_mining_transform.transform_list DEFAULT NULL);
TBD
 
CREATE_MODEL2
Undocumented dbms_data_mining.create_model2(
model_name          IN VARCHAR2,
mining_function     IN VARCHAR2,
data_query          IN CLOB,
set_list            IN sys.dbms_data_mining.setting_list,
case_id_column_name IN VARCHAR2 DEFAULT NULL,
target_column_name  IN VARCHAR2 DEFAULT NULL,
xform_list          IN sys.dbms_data_mining_transform.transform_list DEFAULT NULL);
TBD
 
DROP_ALGORITHM
Undocumented dbms_data_mining.drop_algorithm(
algorithm_name IN VARCHAR2,
cascade        IN BOOLEAN DEFAULT FALSE);
TBD
 
DROP_MODEL
Undocumented dbms_data_mining.drop_model(
model_name IN VARCHAR2,
force      IN BOOLEAN DEFAULT FALSE);
TBD
 
DROP_PARTITION
Undocumented dbms_data_mining.drop_partition(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2);
TBD
 
EXPORT_MODEL
Undocumented dbms_data_mining.export_model(
filename     IN VARCHAR2,
directory    IN VARCHAR2,
model_filter IN VARCHAR2 DEFAULT NULL,
filesize     IN VARCHAR2 DEFAULT NULL,
operation    IN VARCHAR2 DEFAULT NULL,
remote_link  IN VARCHAR2 DEFAULT NULL,
jobname      IN VARCHAR2 DEFAULT NULL);
TBD
 
EXPORT_SERMODEL
Undocumented dbms_data_mining.export_sermodel(
model_data     IN OUT NOCOPY BLOB,
model_name     IN            VARCHAR2,
partition_name IN            VARCHAR2 DEFAULT NULL);
TBD
 
FETCH_ALG_SCHEMA
Undocumented dbms_data_mining.fetch_alg_schema RETURN CLOB;
SELECT dbms_data_mining.fetch_alg_schema;

FETCH_ALG_SCHEMA
--------------------------------------------------------------------------------
{
  "type": "object",
  "properties": {
    "algo_name_display": { "type" : "object",
                           "properties" : {
                              "language" : { "type" : "string",
                                             "enum" : ["English", "Spanish", "French"],
                                             "default" : "English"},
                              "name" : { "type" : "string"}
                         }
                       },
    "function_language": {"type": "string" },
    "mining_function": {
             "type" : "object",
             "properties" :
                 { "type" : "object",
                     "properties" : {
                        "mining_function_name" : { "type" : "string"},
                        "build_function": {
                           "type": "object",
                           "properties": {
                              "function_body": { "type": "CLOB" }
                            }
                          },
                         "detail_function": {
                           "type" : "array",
                           "items" : [
                              {"type": "object",
                               "properties": {
                                  "function_body": { "type": "CLOB" },
                                  "view_columns": {  "type" : "array",
                                                     "items" : {
                                                        "type" : "object",
                                                        "properties" : {
                                                           "name" : { "type" : "string"},
                                                           "type" : { "type" : "string",
                                                                      "enum" :
["VARCHAR2", "NUMBER", "DATE", "BOOLEAN"]
                                              }
                                            }
                                          }
                                        }
                                      }
                                    }
                                  ]
                                },
                               "score_function": {
                                  "type": "object",
                                  "properties": {
                                     "function_body": { "type": "CLOB" }
                                   }
                                 },
                                "weight_function": {
                                   "type": "object",
                                   "properties": {
                                      "function_body": { "type": "CLOB" }
                                    }
                                  }
                                }
                              }
         },
        "algo_setting": {
                 "type" : "array",
                 "items" : [
                     { "type" : "object",
                       "properties" : {
                          "name" : { "type" : "string"},
                          "name_display": { "type" : "object",
                                            "properties" : {
                                               "language" : { "type" : "string",
                                               "enum" : ["English", "Spanish", "French"],
                                               "default" : "English"},
                                               "name" : { "type" : "string"}}
                                          },
                          "data_type" : { "type" : "string",
                                          "enum" : ["string", "integer", "number", "boolean"]},
                          "optional": {"type" : "BOOLEAN",
                                       "default" : "FALSE"},
                          "value" : { "type" : "string"},
                          "min_value" : { "type": "object",
                                          "properties": {
                                             "min_value": {"type": "number"},
                                             "inclusive": { "type": "boolean",
                                                            "default" : TRUE},
                                           }
                                         },
                          "max_value" : {"type": "object",
                                         "properties": {
                                            "max_value": {"type": "number"},
                                            "inclusive": { "type": "boolean",
                                                           "default" : TRUE},
                                          }
                                        },
                          "categorical choices" : { "type": "array",
                                                    "items": {
                                                       "type": "string"
                                          }
                                        },
                          "description_display": { "type" : "object",
                                                   "properties" : {
                                                      "language" : { "type" : "string",
                                                                     "enum" : ["
English", "Spanish", "French"],
                                                                     "default" :
"English"},
                                                      "name" : { "type" : "string"}}
                                        }
          }
        }
      ]
    }
  }
}
 
GET_ASSOCIATION_RULES
Undocumented dbms_data_mining.get_association_rules(
model_name       IN VARCHAR2,
topn             IN NUMBER                     DEFAULT NULL,
rule_id          IN INTEGER                    DEFAULT NULL,
min_confidence   IN NUMBER                     DEFAULT NULL,
min_support      IN NUMBER                     DEFAULT NULL,
max_rule_length  IN INTEGER                    DEFAULT NULL,
min_rule_length  IN INTEGER                    DEFAULT NULL,
sort_order       IN sys.ora_mining_varchar2_nt DEFAULT NULL,
antecedent_items IN sys.dm_items               DEFAULT NULL,
consequent_items IN sys.dm_items               DEFAULT NULL,
min_lift         IN NUMBER                     DEFAULT NULL,
partition_name   IN VARCHAR2                   DEFAULT NULL)
RETURN sys.dm_rules PIPELINED;
TBD
 
GET_DEFAULT_SETTINGS
Undocumented dbms_data_miningget_default_settings RETURN sys.dm_model_settings PIPELINED;
SELECT * FROM TABLE(dbms_data_mining.get_default_settings);

no rows selected
 
GET_FREQUENT_ITEMSETS
Specifying topn orders by support DESC otherwise there is no ordering dbms_data_mining.get_frequent_itemsets(
model_name         IN VARCHAR2,
topn               IN NUMBER   DEFAULT NULL,
max_itemset_length IN NUMBER   DEFAULT NULL,
partition_name     IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_itemsets PIPELINED;
TBD
 
GET_MODEL_COST_MATRIX
Undocumented dbms_data_mining.get_model_cost_matrix(
model_name     IN VARCHAR2,
matrix_type    IN VARCHAR2 DEFAULT cost_matrix_type_score,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_cost_matrix PIPELINED;
TBD
 
GET_MODEL_DETAILS_AI
Undocumented dbms_data_mining.get_model_details_ai(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_ranked_attributes PIPELINED;
TBD
 
GET_MODEL_DETAILS_EM
Undocumented dbms_data_mining.get_model_details_em(
model_name        IN VARCHAR2,
cluster_id        IN NUMBER   DEFAULT NULL,
attribute         IN VARCHAR2 DEFAULT NULL,
centroid          IN NUMBER   DEFAULT 1,
histogram         IN NUMBER   DEFAULT 1,
rules             IN NUMBER   DEFAULT 2,
attribute_subname IN VARCHAR2 DEFAULT NULL,
topn_attributes   IN NUMBER   DEFAULT NULL,
partition_name    IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_clusters PIPELINED;
TBD
 
GET_MODEL_DETAILS_EM_COMP
Undocumented dbms_data_mining.get_model_details_em_comp(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_em_component_set PIPELINED;
TBD
 
GET_MODEL_DETAILS_EM_PROJ
Undocumented dbms_data_mining.get_model_details_em_proj(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_em_project_set PIPELINED;
TBD
 
GET_MODEL_DETAILS_GLM
Undocumented dbms_data_mining.get_model_details_glm(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_glm_coeff_set PIPELINED;
TBD
 
GET_MODEL_DETAILS_GLOBAL
Undocumented dbms_data_mining.get_model_details_global(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_model_global_details PIPELINED;
TBD
 
GET_MODEL_DETAILS_KM
Undocumented dbms_data_mining.get_model_details_km(
model_name        IN VARCHAR2,
cluster_id        IN NUMBER   DEFAULT NULL,
attribute         IN VARCHAR2 DEFAULT NULL,
centroid          IN NUMBER   DEFAULT 1,
histogram         IN NUMBER   DEFAULT 1,
rules             IN NUMBER   DEFAULT 2,
attribute_subname IN VARCHAR2 DEFAULT NULL,
topn_attributes   IN NUMBER   DEFAULT NULL,
partition_name    IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_clusters PIPELINED;
TBD
 
GET_MODEL_DETAILS_NB
Undocumented dbms_data_mining.get_model_details_nb(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_nb_details PIPELINED;
TBD
 
GET_MODEL_DETAILS_NMF
Undocumented dbms_data_mining.get_model_details_nmf(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_nmf_feature_set PIPELINED;
TBD
 
GET_MODEL_DETAILS_OC
Undocumented dbms_data_mining.get_model_details_oc(
model_name      IN VARCHAR2,
cluster_id      IN NUMBER   DEFAULT NULL,
attribute       IN VARCHAR2 DEFAULT NULL,
centroid        IN NUMBER   DEFAULT 1,
histogram       IN NUMBER   DEFAULT 1,
rules           IN NUMBER   DEFAULT 2,
topn_attributes IN NUMBER   DEFAULT NULL,
partition_name  IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_clusters PIPELINED;
TBD
 
GET_MODEL_DETAILS_RA
Undocumented dbms_data_mining.get_model_details_ra(
model_name IN VARCHAR2,
par_cur    IN sys_refcursor,
out_qry    IN VARCHAR2,
view_num   IN NUMBER DEFAULT -1)
RETURN sys.anyDataSet PIPELINED USING sys.dm$rqmod_detailimpl;
TBD
 
GET_MODEL_DETAILS_SVD
Undocumented dbms_data_mining.get_model_details_svd(
model_name     IN VARCHAR2,
matrix_type    IN VARCHAR2 DEFAULT NULL,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_svd_matrix_set PIPELINED;
TBD
 
GET_MODEL_DETAILS_SVM
Undocumented dbms_data_mining.get_model_details_svm(
model_name     IN VARCHAR2,
reverse_coef   IN NUMBER   DEFAULT 0,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_svm_linear_coeff_set PIPELINED;
TBD
 
GET_MODEL_DETAILS_XML
XML (PMML) versions of get model details dbms_data_mining.get_model_details_xml(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.xmlType;
TBD
 
GET_MODEL_R_FUNCTION
Undocumented dbms_data_mining.get_model_r_function(
model_name      IN VARCHAR2,
r_function_type IN VARCHAR2)
RETURN VARCHAR2;
TBD
 
GET_MODEL_SETTINGS
Undocumented dbms_data_mining.get_model_settings(model_name IN VARCHAR2)
RETURN sys.dm_model_settings PIPELINED;
TBD
 
GET_MODEL_SIGNATURE
Undocumented dbms_data_mining.get_model_signature(model_name IN VARCHAR2)
RETURN sys.dm_model_signature PIPELINED;
TBD
 
GET_MODEL_TRANSFORMATIONS
Undocumented dbms_data_mining.get_model_transformations(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL)
RETURN sys.dm_transforms PIPELINED;
TBD
 
GET_TRANSFORM_LIST
Undocumented dbms_data_mining.get_transform_list(
xform_list   OUT NOCOPY sys.transform_list,
model_xforms IN         sys.dm_transforms);
TBD
 
IMPORT_MODEL
Undocumented

Overload 1		 dbms_data_mining.import_model(
filename         IN VARCHAR2,
directory        IN VARCHAR2,
model_filter     IN VARCHAR2 DEFAULT NULL,
operation        IN VARCHAR2 DEFAULT NULL,
remote_link      IN VARCHAR2 DEFAULT NULL,
jobname          IN VARCHAR2 DEFAULT NULL,
schema_remap     IN VARCHAR2 DEFAULT NULL,
tablespace_remap IN VARCHAR2 DEFAULT NULL);
TBD
Undocumented

Overload 2		 dbms_data_mining.import_model(
model_name   IN VARCHAR2,
pmmldoc      IN sys.xmltype,
strict_check IN BOOLEAN DEFAULT FALSE);
TBD
 
IMPORT_ONNX_MODEL (new 23ai)
Undocumented dbms_data_mining.import_onyx_model(
model_name IN VARCHAR2,
model_data IN BLOB,
metadata   IN JSON);
TBD
 
IMPORT_SERMODEL
Undocumented dbms_data_mining.import_sermodel(
model_data IN BLOB,
model_name IN VARCHAR2);
TBD
 
RANK_APPLY
Undocumented dbms_data_mining.rank_apply(
apply_result_table_name     IN VARCHAR2,
case_id_column_name         IN VARCHAR2,
score_column_name           IN VARCHAR2,
score_criterion_column_name IN VARCHAR2,
ranked_apply_table_name     IN VARCHAR2,
top_n                       IN INTEGER  DEFAULT 1,
cost_matrix_table_name      IN VARCHAR2 DEFAULT NULL,
apply_result_schema_name    IN VARCHAR2 DEFAULT NULL,
cost_matrix_schema_name     IN VARCHAR2 DEFAULT NULL);
TBD
 
REGISTER_ALGORITHM
Undocumented dbms_data_mining.register_algorithm(
algorithm_name        IN VARCHAR2,
algorithm_metadata    IN CLOB,
algorithm_description IN VARCHAR2 DEFAULT NULL);
TBD
 
REMOVE_COST_MATRIX
Undocumented dbms_data_mining.remove_cost_matrix(
model_name     IN VARCHAR2,
partition_name IN VARCHAR2 DEFAULT NULL);
TBD
 
RENAME_MODEL
Undocumented dbms_data_mining.rename_model(
model_name           IN VARCHAR2,
new_model_name       IN VARCHAR2,
versioned_model_name IN VARCHAR2 DEFAULT NULL);
TBD

Related Topics
Built-in Functions
Built-in Packages
DBMS_DATA_MINING_INTERNAL
DBMS_DATA_MINING_TRANSFORM
DMP_SEC
DMP_SYS
ODM_MODEL_UTIL
What's New In 21c
What's New In 26ai

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