Index
A
- ADP 4.5.2
- ALGO_EXTENSIBLE_LANG 4.8.6.1
- algorithms 4.1, 4.3
- ALL_MINING_MODEL_ATTRIBUTES 2.2
- ALL_MINING_MODEL_PARTITIONS 2.2
- ALL_MINING_MODEL_SETTINGS 2.2, 4.8.5
- ALL_MINING_MODEL_VIEWS 2.2
- ALL_MINING_MODEL_XFORMS 2.2
- ALL_MINING_MODELS 2.2
- anomaly detection 2.1, 3.1.3, 4.2, 4.3, 5.7
- APPLY 5.1
- APPROX_COUNT 2.5
- APPROX_RANK 2.5
- APPROX_SUM 2.5
- Apriori 3.4, 4.2, 4.3, 4.4.3
- example: calculating aggregates 3.5.1
- association rules 4.2, 4.3
- model detail view 4.9.1
- attribute importance 2.1, 4.2, 4.3
- attributes 3.1.1, 3.2, 6.3
- attribute specification 4.5.3, 6.5
- AUDIT 8.4.2, 8.5.2
- Automatic Data Preparation 1.1, 3.1.3, 4.4
C
- case ID 3.1, 3.1.1, 3.2.4, 5.7
- case table 3.1, 3.8
- categorical attributes 6.1
- classification 2.1, 3.1.2, 3.1.3, 3.2.2, 4.2, 4.3
- class weights 4.8.3
- clipping 4.5.4.3
- CLUSTER_DETAILS 1.4, 2.4
- CLUSTER_DISTANCE 2.4
- CLUSTER_ID 1.4, 2.4
- CLUSTER_PROBABILITY 2.4
- CLUSTER_SET 1.4, 2.4
- clustering 1.4, 2.1, 3.1.3, 4.3
- COMMENT 8.4.2
- CORR 2.5
- CORR_K 2.5
- CORR_S 2.5
- cost matrix 4.8.1, 5.6, 8.4.3
- cost-sensitive prediction 5.6
- COVAR_POP 2.5
- COVAR_SAMP 2.5
- CUR Matrix Decomposition 4.2, 4.3, 4.4.3
D
- data
- Database Upgrade Assistant 8.2.2.1
- Data preparation
- model view
- text features 4.9.27
- model view
- data types 3.1.1, 3.8.1
- nested 3.3
- DBMS_DATA_MINING 2.3, 2.3.1, 4.2
- DBMS_DATA_MINING_TRANSFORM 2.3, 2.3.2
- DBMS_PREDICTIVE_ANALYTICS 1.3, 2.3, 2.3.3
- Decision Tree 4.2, 4.3, 4.4.3, 5.4
- directory objects 8.3.4
- DM$VA 4.9.8, 4.9.11, 4.9.12, 4.9.16, 4.9.18, 4.9.22
- DM$VB 4.9.10, 4.9.16, 4.9.18, 4.9.22
- DM$VC 4.9.7, 4.9.9, 4.9.10, 4.9.11, 4.9.12, 4.9.14
- DM$VD 4.9.8, 4.9.16, 4.9.18
- DM$VE 4.9.20
- DM$VF 4.9.16
- DM$VG 4.9.7, 4.9.8, 4.9.9, 4.9.10, 4.9.11, 4.9.12, 4.9.14, 4.9.16, 4.9.18, 4.9.20, 4.9.22, 4.9.26
- DM$VH 4.9.16, 4.9.18
- DM$VI 4.9.7, 4.9.14, 4.9.16, 4.9.20
- DM$VM 4.9.7, 4.9.16
- DM$VN 4.9.8, 4.9.9, 4.9.11, 4.9.16, 4.9.20
- DM$VO 4.9.7, 4.9.16
- DM$VP 4.9.7, 4.9.10, 4.9.16, 4.9.26
- DM$VR 4.9.16, 4.9.18, 4.9.26
- DM$VS 4.9.7, 4.9.8, 4.9.9, 4.9.10, 4.9.11, 4.9.12, 4.9.14, 4.9.16, 4.9.18, 4.9.20, 4.9.22
- DM$VT 4.9.7, 4.9.9, 4.9.10, 4.9.11, 4.9.12, 4.9.14, 4.9.26
- DM$VV 4.9.10
- DM$VW 4.9.7, 4.9.8, 4.9.9, 4.9.10, 4.9.11, 4.9.12, 4.9.14, 4.9.16, 4.9.18, 4.9.20, 4.9.22
- downgrading 8.2.4
- DROP_ONNX_MODEL 7.1.5
E
M
- machine learning
- machine learning for SQL
- privileges for A.2
- machine learning for SQL models
- machine learning functions 4.1, 4.2
- machine learning models
- auditing 8.5.2
- machine learning models for SQL
- machine learning techniques 2.1
- market basket data 3.4
- MDL 4.4.3
- memory 8.1.2
- Minimum Description Length 4.3, 4.4.3
- missing value treatment 3.6.2
- model attributes
- model details 3.2.6
- model detail views 4.9
- association rules 4.9.1
- clustering algorithms 4.9.15
- CUR Matrix Decomposition 4.9.6
- Decision Tree 4.9.7
- EM 4.9.16
- ESM 4.9.26
- Explicit Semantic Analysis 4.9.19
- Exponential Smoothing 4.9.26
- for binning 4.9.23
- for classification algorithms 4.9.5
- for frequent itemsets 4.9.2
- for global information 4.9.24
- for normalization and missing value handling 4.9.25
- for transactional itemsets 4.9.3
- for transactional rules and itemsets 4.9.4
- GLM 4.9.8
- k-Means 4.9.17
- Minimum Description Length 4.9.22
- MSET-SPRT 4.9.9
- Naive Bayes 4.9.10
- Neural Network 4.9.11
- Non-Negative Matrix Factorization 4.9.20
- O-Cluster 4.9.18
- Random Forest 4.9.12
- SVD 4.9.21
- SVM 4.9.13
- XGBoost 4.9.14
- model detail views for Random Forest 4.9.12
- models
- model signature 3.2.4
- MSET-SPRT 4.3
- Multivariate State Estimation Technique - Sequential Probability Ratio Test 4.2, 4.4.3
O
P
- parallel execution 5.1, 8.1.2
- partitioned model 4.8.4
- partitions
- data dictionary 2.2
- PGA 8.1.2
- PL/SQL packages 2.3
- PMML 8.3.7
- PREDICTION 1.2, 1.3, 2.4, 5.5
- PREDICTION_BOUNDS 2.4
- PREDICTION_COST 2.4
- PREDICTION_DETAILS 2.4, 5.5
- PREDICTION_PROBABILITY 1.3, 2.4, 5.4
- PREDICTION_SET 2.4
- PREDICTION function
- GROUPING hint 5.3.4
- predictive analytics 1.1, 1.3, 2.1, 2.3.3
- preparing data
- using retail analysis data aggregates 3.5
- prior probabilities 4.8.2
- priors table 4.8.2
- privileges 8.4.1
R
- RALG_BUILD_FUNCTION 4.8.6.2
- RALG_BUILD_PARAMETER 4.8.6.2.1
- RALG_DETAILS_FORMAT 4.8.6.4
- RALG_DETAILS_FUNCTION 4.8.6.3
- RALG_SCORE_FUNCTION 4.8.6.5
- RALG_WEIGHT_FUNCTION 4.8.6.6
- Random Forest 4.2, 4.3, 4.4.3, 4.9.12
- REGISTER_ALGORITHM procedure 4.8.6.8
- regression 2.1, 3.1.2, 3.1.3, 3.2.2, 4.2, 4.3
- reverse transformations 3.2.6
- R extensible language 4.3
- R machine learning model
- settings 4.8.6
S
- scoring 1.1, 2.1, 5, 8.1.2, 8.4.3
- settings
- SGA 8.1.2
- Singular Value Decomposition 4.4.3
- sparse data 3.6
- SQL AUDIT 2.1, 8.5.2
- SQL COMMENT 2.1, 8.5.1
- SQL Developer 1.1
- SQL scoring function 2.4
- STACK 2.3.2.1, 4.5.1.2
- Static Dictionary Views
- ALL_MINING_MODEL_VIEWS 2.2.5
- STATS_BINOMIAL_TEST 2.5
- STATS_CROSSTAB 2.5
- STATS_F_TEST 2.5
- STATS_KS_TEST 2.5
- STATS_MODE 2.5
- STATS_MW_TEST 2.5
- STATS_ONE_WAY_ANOVA 2.5
- STATS_T_TEST_* 2.5
- STATS_T_TEST_INDEP 2.5
- STATS_T_TEST_INDEPU 2.5
- STATS_T_TEST_ONE 2.5
- STATS_T_TEST_PAIRED 2.5
- STATS_WSR_TEST 2.5
- STDDEV 2.5
- STDDEV_POP 2.5
- STDDEV_SAMP 2.5
- SUM 2.5
- Support Vector Machine 4.2, 4.3, 4.4.3
- SVD 4.3
- system privileges 8.4.2, A.2