Index

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

Numerics

  • 3rd party package 6.3
  • 3rd party packages 6.2

A

  • ADMIN 6.2
  • algorithms
    • Apriori 9.8
    • attribute importance 9.7
    • Automated Machine Learning 10.1
    • automatically selecting 10.5
    • Automatic Data Preparation 9.5
    • Decision Tree 9.9
    • Expectation Maximization 9.10
    • Explicit Semantic Analysis 9.11
    • Exponential Smoothing 9.20
    • Generalized Linear Model 9.12
    • k-Means 9.13
    • machine learning 9.1
    • Minimum Description Length 9.7
    • Naive Bayes 9.14
    • Neural Network 9.15
    • Non-Negative Matrix Factorization 9.19
    • Random Forest 9.16
    • settings common to all 9.3
    • Singular Value Decomposition 9.17
    • Support Vector Machine 9.18
    • XGBoost 9.21
  • algorithm selection class 10.2
  • ALL_PYQ_DATASTORE_CONTENTS view 11.3.1
  • ALL_PYQ_DATASTORES view 11.3.2
  • ALL_PYQ_SCRIPTS view 11.4.1
  • anomaly detection models 9.18
  • Apriori algorithm 9.8
  • attribute importance 9.7
  • Automated Machine Learning
  • Automatic Data Preparation algorithm 9.5
  • Automatic Machine Learning
    • connection parameter 7.2.1
  • Autonomous Database 7.1

C

  • classes
  • classification algorithm 9.16
  • classification and regression algorithm 9.21
  • classification and regression models 9.21
  • classification models 9.5, 9.9, 9.12, 9.14, 9.15, 9.16, 9.18
  • client
    • installing for Linux for Autonomous Database 3
    • installing for Linux on-premises 4.5.1.2
  • Clustering algorithm 9.20
  • clustering models 9.10, 9.11, 9.13
  • Clustering models 9.20
  • conda enviroment 6.2
  • connection
    • creating a on-premises database 7.2.3
    • functions 7.2.1
  • control arguments 11.5.1
  • convert Python to SQL 2.3
  • creating
  • cx_Oracle.connect function 7.2.1
  • cx_Oracle package 7.2.1

D

  • data
  • database
    • connecting to an on-premises 7.2.3
  • data parallel processing 11.5.1
  • datastores
  • Date Types 8.2.7
  • DCLI
  • Decision Tree algorithm 9.9
  • Distributed Command Line Interface 5.2
  • Download environment from object storage 6.3
  • dropping

E

  • Embedded Python Execution
  • EM model 9.10
  • ESA model 9.11
  • Exadata 5.1
  • Expectation Maximization algorithm 9.10
  • explainability 9.6
  • Explicit Semantic Analysis algorithm 9.11
  • Exponential Smoothing Model 9.20
  • exporting models 9.4

F

G

  • GLM models 9.12
  • granting
    • access to scripts and datastores 7.4.7
    • user privileges 4.4.4
  • graphics
    • rendering 8.3

I

  • importing models 9.4
  • installing
    • client for Linux for Autonomous Database 3
    • client for Linux on-premises 4.5.1.2
    • server for Linux on-premises 4.4.2
  • Instant Client
    • installing for Linux on-premises 4.5.1.1

K

L

  • libraries inOML4Py 2.4
  • Linux
    • installing Python for 4.2
    • requirements 4.1
    • uninstalling on-premises client for 4.5.3
    • uninstalling on-premises server for 4.4.6
  • Linux for Autonomous Database
    • installing client for 3
  • Linux on-premises
    • installing client for 4.5.1.2
    • installing Oracle Instant Client for 4.5.1.1
    • installing server for 4.4.2
    • supporting packages for 4.3

M

N

  • Naive Bayes model 9.14
  • Neural Network model 9.15
  • NMF models 9.19

O

  • oml_input_type argument 11.5.1
  • oml_na_omit argument 11.5.1
  • oml.ai class 9.7
  • oml.ar class 9.8
  • oml.automl.AlgorithmSelection class 10.2
  • oml.automl.FeatureSelection class 10.3
  • oml.automl.ModelSelection class 10.5
  • oml.automl.ModelTuning class 10.4
  • oml.boxplot function 8.3
  • oml.check_embed function 7.2.1, 7.2.3
  • oml.connect function 7.2.1, 7.2.3
  • oml.create function 7.3.5
  • oml.cursor function 7.3.1, 7.3.5
  • oml.Datetime 8.2.7
  • oml.dir function 7.3.1, 7.3.4
  • oml.disconnect function 7.2.1, 7.2.3
  • oml.do_eval function 11.5.2
  • oml.drop function 7.3.5
  • oml.ds.delete function 7.4.6
  • oml.ds.describe function 7.4.5
  • oml.ds.dir function 7.4.4
  • oml.ds.load function 7.4.3
  • oml.ds.save function 7.4.2
  • oml.dt class 9.5, 9.9
  • oml.em class 9.10
  • oml.esa class 9.11
  • oml.esm class 9.20
  • oml.glm class 9.12
  • oml.grant function 7.4.7
  • oml.graphics class 8.3
  • oml.group_apply function 11.5.4
  • oml.hist function 8.3
  • oml.index_apply function 11.5.6
  • oml.Integer 8.2.7
  • oml.isconnected function 7.2.1, 7.2.3
  • oml.km class 9.13
  • oml.nb class 9.14
  • oml.nn class 9.15
  • oml.push function 7.3.2
  • oml.revoke function 7.4.7
  • oml.rf class 9.16
  • oml.row_apply function 11.5.5
  • oml.script.create function 11.5.7.2
  • oml.script.dir function 11.5.7.3
  • oml.script.drop function 11.5.7.5
  • oml.script.load function 11.5.7.4
  • oml.set_connection function 7.2.1, 7.2.3
  • oml.svd class 9.17
  • oml.svm class 9.18
  • oml.sync function 7.3.4
  • oml.table_apply function 11.5.3
  • oml.Timedelta 8.2.7
  • oml.Timezone 8.2.7
  • oml.xgb class 9.21
  • OML4Py 2, 5.1
  • on-premises client
  • on-premises server
  • on-premises system requirements 4.1
  • Oracle Machine Learning Notebooks 7.1
  • Oracle Machine Learning Python interpreter 7.1
  • Oracle wallets
  • ore.nmf function 9.19

P

  • packages
    • supporting for Linux on-premises 4.3
  • parallel processing 11.5.1
  • parametric models 9.12
  • PL/SQL procedures
  • predict.proba method 9.14
  • predict method 9.14
  • privileges
  • proxy objects 2.3
  • pull method 7.3.3
  • PYQADMIN role 4.4.4
  • pyqEval function 11.6.2
  • pyqGrant function 11.6.6, 11.7.2.7
  • pyqGroupEval function 11.6.5
  • pyqRowEval function 11.6.4
  • pyqTableEval function 11.6.3
  • pyquser.sql script 4.4.5
  • Python 5.1
    • installing for Linux 4.2
    • libraries in OML4Py 2.4
    • requirements 4.1
    • version used 2.4
  • Python interpreter 7.1
  • Python objects
  • python packages 6.2
  • Python to SQL conversion 2.3

R

  • Random Forest algorithm 9.16
  • ranking
    • attribute importance 9.7
  • read privilege
    • granting or revoking 7.4.7
  • regression models 9.12, 9.15
  • requirements
    • on-premises system 4.1
  • resources
    • managing 12
  • revoking
    • access to scripts and datastores 7.4.7
  • roles

S

  • scoring new data 2.2, 9.1
  • script repository
    • granting or revoking access to 7.4.7
    • managing user-defined Python functions in 11.5.7.1
    • registering a user-defined function 11.5.7.1
  • scripts
  • server
    • installing for Linux on-premises 4.4.2
  • settings
    • about model 9.2
    • Apriori algorithm 9.8
    • association rules 9.8
    • Automatic data preparation algorithm 9.5
    • Decision Tree algorithm 9.9
    • Expectation Maximization model 9.10
    • Explicit Semantic Analysis algorithm 9.11
    • Exponential Smoothing Model 9.20
    • Generalized Linear Model algorithm 9.12
    • k-Means algorithm 9.13
    • Minimum Description Length algorithm 9.7
    • Naive Bayes algorithm 9.14
    • Neural Network algorithm 9.15
    • Random Forest algorithm 9.16
    • shared algorithm 9.3
    • Singular Value Decomposition algorithm 9.17
    • sttribute importance 9.7
    • Support Vector Machine algorithm 9.18
    • XGBoost algorithm 9.21
  • special control arguments 11.5.1
  • SQL APIs
  • SQL to Python conversion 2.3
  • supporting packages
    • for Linux on-premises 4.3
  • SVD model 9.17
  • SVM models 9.18
  • synchronizing database tables 7.3.4
  • sys.pyqScriptCreate procedure 11.6.8
  • sys.pyqScriptDrop procedure 11.6.9

T

U

  • uninstalling
    • on-premises client 4.5.3
    • on-premises server 4.4.6
  • USER_PYQ_DATASTORES view 11.3.3
  • USER_PYQ_SCRIPTS view 11.4.2
  • user-defined Python functions
    • Embedded Python Execution of 11.5.1
  • users

V

W

  • wallets

X

  • XGBoost algorithm 9.21