Index

Numerics  A  C  D  E  F  G  I  K  L  M  N  O  P  R  S  T  U  V  X  

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Numerics

• 3rd party package 2.3
• 3rd party packages 2.2

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A

• ADMIN 2.2
• algorithms
  • Apriori 5.8
  • attribute importance 5.7
  • Automated Machine Learning 6.1
  • automatically selecting 6.5
  • Automatic Data Preparation 5.5
  • Decision Tree 5.9
  • Expectation Maximization 5.10
  • Explicit Semantic Analysis 5.11
  • Exponential Smoothing 5.20
  • Generalized Linear Model 5.12
  • k-Means 5.13
  • machine learning 5.1
  • Minimum Description Length 5.7
  • Naive Bayes 5.14
  • Neural Network 5.15
  • Non-Negative Matrix Factorization 5.19
  • Random Forest 5.16
  • settings common to all 5.3
  • Singular Value Decomposition 5.17
  • Support Vector Machine 5.18
  • XGBoost 5.21
• algorithm selection class 6.2
• ALL_PYQ_DATASTORE_CONTENTS view 7.2.1
• ALL_PYQ_DATASTORES view 7.2.2
• ALL_PYQ_SCRIPTS view 7.3.1
• anomaly detection models 5.18
• Apriori algorithm 5.8
• attribute importance 5.7
• Automated Machine Learning
  • about 6.1
• Automatic Data Preparation algorithm 5.5
• Autonomous Database 3.1

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C

• classes
  • Automated Machine Learning 6.1
  • GlobalFeatureImportance 5.6
  • machine learning 5.1
  • oml.ai 5.7
  • oml.ar 5.8
  • oml.automl.AlgorithmSelection 6.2
  • oml.automl.FeatureSelection 6.3
  • oml.automl.ModelSelection 6.5
  • oml.automl.ModelTuning 6.4
  • oml.dt 5.5, 5.9
  • oml.em 5.10
  • oml.esa 5.11
  • oml.esm 5.20
  • oml.glm 5.12
  • oml.graphics 4.3
  • oml.km 5.13
  • oml.nb 5.14
  • oml.nn 5.15
  • oml.rf 5.16
  • oml.svd 5.17
  • oml.svm 5.18
  • oml.xgb 5.21
• classification algorithm 5.16
• classification and regression algorithm 5.21
• classification and regression models 5.21
• classification models 5.5, 5.9, 5.12, 5.14, 5.15, 5.16, 5.18
• Clustering algorithm 5.20
• clustering models 5.10, 5.11, 5.13
• Clustering models 5.20
• conda enviroment 2.2
• control arguments 7.4.1
• convert Python to SQL 1.3
• creating
  • proxy objects 3.2.4, 3.2.5

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D

• data
  • about moving 3.2.1
  • exploring 4.2.1
  • filtering 4.1.4
  • preparing 4.1.1
  • selecting 4.1.2
• data parallel processing 7.4.1
• datastores
  • about 3.3.1
  • database views for 7.2.1, 7.2.2, 7.2.3
  • deleting objects 3.3.6
  • describing objects in 3.3.5
  • getting information about 3.3.4
  • granting or revoking access to 3.3.7
  • loading objects from 3.3.3
  • saving objects in 3.3.2
• Date Types 4.2.7
• Decision Tree algorithm 5.9
• Download environment from object storage 2.3
• dropping
  • tables 3.2.5

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E

• Embedded Python Execution
  • about 7.4.1
  • SQL interface for 7.5
• EM model 5.10
• ESA model 5.11
• Expectation Maximization algorithm 5.10
• explainability 5.6
• Explicit Semantic Analysis algorithm 5.11
• Exponential Smoothing Model 5.20
• exporting models 5.4

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F

• feature extraction algorithm 5.11
• feature extraction class 5.17
• feature selection class 6.3
• function
  • pyqGrant 7.5.2.7
• functions
  • Embedded Python Execution 7.4.1
  • for graphics 4.3
  • for managing user-defined Python functions 7.4.7.1
  • oml.boxplot 4.3
  • oml.create 3.2.5
  • oml.cursor 3.2.1, 3.2.5
  • oml.dir 3.2.1, 3.2.4
  • oml.do_eval 7.4.2
  • oml.drop 3.2.5
  • oml.ds.delete 3.3.6
  • oml.ds.describe 3.3.5
  • oml.ds.dir 3.3.4
  • oml.ds.load 3.3.3
  • oml.ds.save 3.3.2
  • oml.grant 3.3.7
  • oml.group_apply 7.4.4
  • oml.hist 4.3
  • oml.index_apply 7.4.6
  • oml.row_apply 7.4.5
  • oml.script.create 7.4.7.2
  • oml.script.dir 7.4.7.3
  • oml.script.drop 7.4.7.5
  • oml.script.load 7.4.7.4
  • oml.sync 3.2.4
  • oml.table_apply 7.4.3

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G

• GLM models 5.12
• granting
  • access to scripts and datastores 3.3.7
• graphics
  • rendering 4.3

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I

• importing models 5.4

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K

• KM model 5.13

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L

• libraries inOML4Py 1.4

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M

• machine learning
  • classes 5.1
• methods
  • drop 4.1.4
  • drop_duplicates 4.1.4
  • dropna 4.1.4
  • for exploring data 4.2.1
  • for preparing data 4.1.1
  • pull 3.2.3
• Minimum Description Length algorithm 5.7
• models
  • association rules 5.8
  • attribute importance 5.7
  • Clustering 5.20
  • Decision Tree 5.5, 5.9
  • Expectation Maximization 5.10
  • explainability 5.6
  • Explicit Semantic Analysis 5.11
  • exporting and importing 5.4
  • for anomaly detection 5.18
  • for classification 5.5, 5.9, 5.12, 5.14, 5.15, 5.16, 5.18
  • for classification and regression 5.21
  • for clustering 5.10, 5.13
  • for Clustering 5.20
  • for feature extraction 5.11, 5.17
  • for regression 5.12, 5.15, 5.18
  • Generalized Linear Model 5.12
  • k-Means 5.13
  • Naive Bayes 5.14
  • Neural Network 5.15
  • Non-Negative Matrix Factorization 5.19
  • parametric 5.12
  • persisting 5.1
  • Random Forest 5.16
  • Singular Value Decomposition 5.17
  • Support Vector Machine 5.18
  • XGBoost 5.21
• model selection 6.5
• model tuning 6.4
• moving data
  • about 3.2.1
  • to a local Python session 3.2.3
  • to the database 3.2.2

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N

• Naive Bayes model 5.14
• Neural Network model 5.15
• NMF models 5.19

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O

• oml_input_type argument 7.4.1
• oml_na_omit argument 7.4.1
• oml.ai class 5.7
• oml.ar class 5.8
• oml.automl.AlgorithmSelection class 6.2
• oml.automl.FeatureSelection class 6.3
• oml.automl.ModelSelection class 6.5
• oml.automl.ModelTuning class 6.4
• oml.boxplot function 4.3
• oml.create function 3.2.5
• oml.cursor function 3.2.1, 3.2.5
• oml.Datetime 4.2.7
• oml.dir function 3.2.1, 3.2.4
• oml.do_eval function 7.4.2
• oml.drop function 3.2.5
• oml.ds.delete function 3.3.6
• oml.ds.describe function 3.3.5
• oml.ds.dir function 3.3.4
• oml.ds.load function 3.3.3
• oml.ds.save function 3.3.2
• oml.dt class 5.5, 5.9
• oml.em class 5.10
• oml.esa class 5.11
• oml.esm class 5.20
• oml.glm class 5.12
• oml.grant function 3.3.7
• oml.graphics class 4.3
• oml.group_apply function 7.4.4
• oml.hist function 4.3
• oml.index_apply function 7.4.6
• oml.Integer 4.2.7
• oml.km class 5.13
• oml.nb class 5.14
• oml.nn class 5.15
• oml.push function 3.2.2
• oml.revoke function 3.3.7
• oml.rf class 5.16
• oml.row_apply function 7.4.5
• oml.script.create function 7.4.7.2
• oml.script.dir function 7.4.7.3
• oml.script.drop function 7.4.7.5
• oml.script.load function 7.4.7.4
• oml.svd class 5.17
• oml.svm class 5.18
• oml.sync function 3.2.4
• oml.table_apply function 7.4.3
• oml.Timedelta 4.2.7
• oml.Timezone 4.2.7
• oml.xgb class 5.21
• OML4Py 1
• Oracle Machine Learning Notebooks 3.1
• Oracle Machine Learning Python interpreter 3.1
• ore.nmf function 5.19

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P

• parallel processing 7.4.1
• parametric models 5.12
• predict.proba method 5.14
• predict method 5.14
• proxy objects 1.3
  • for database tables 3.2.4, 3.2.5
  • storing 3.3.1
• pull method 3.2.3
• pyqGrant function 7.5.2.7
• Python
  • libraries in OML4Py 1.4
  • version used 1.4
• Python interpreter 3.1
• Python objects
  • storing 3.3.1
• python packages 2.2
• Python to SQL conversion 1.3

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R

• Random Forest algorithm 5.16
• ranking
  • attribute importance 5.7
• read privilege
  • granting or revoking 3.3.7
• regression models 5.12, 5.15
• resources
  • managing 8
• revoking
  • access to scripts and datastores 3.3.7

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S

• scoring new data 1.2, 5.1
• script repository
  • granting or revoking access to 3.3.7
  • managing user-defined Python functions in 7.4.7.1
  • registering a user-defined function 7.4.7.1
• settings
  • about model 5.2
  • Apriori algorithm 5.8
  • association rules 5.8
  • Automatic data preparation algorithm 5.5
  • Decision Tree algorithm 5.9
  • Expectation Maximization model 5.10
  • Explicit Semantic Analysis algorithm 5.11
  • Exponential Smoothing Model 5.20
  • Generalized Linear Model algorithm 5.12
  • k-Means algorithm 5.13
  • Minimum Description Length algorithm 5.7
  • Naive Bayes algorithm 5.14
  • Neural Network algorithm 5.15
  • Random Forest algorithm 5.16
  • shared algorithm 5.3
  • Singular Value Decomposition algorithm 5.17
  • sttribute importance 5.7
  • Support Vector Machine algorithm 5.18
  • XGBoost algorithm 5.21
• special control arguments 7.4.1
• SQL APIs
  • pyqGrant function 7.5.2.7
• SQL to Python conversion 1.3
• SVD model 5.17
• SVM models 5.18
• synchronizing database tables 3.2.4

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T

• tables
  • creating 3.2.5
  • dropping 3.2.4, 3.2.5
  • proxy objects for 3.2.4, 3.2.5
• task parallel processing 7.4.1
• transparency layer 1.3

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U

• USER_PYQ_DATASTORES view 7.2.3
• USER_PYQ_SCRIPTS view 7.3.2
• user-defined Python functions
  • Embedded Python Execution of 7.4.1

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V

• views
  • ALL_PYQ_DATASTORE_CONTENTS 7.2.1
  • ALL_PYQ_DATASTORES 7.2.2
  • ALL_PYQ_SCRIPTS 7.3.1
  • USER_PYQ_DATASTORES 7.2.3
  • USER_PYQ_SCRIPTS 7.3.2

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X

• XGBoost algorithm 5.21