Index

Numerics

3rd party package 5.3
3rd party packages 5.2

A

ADMIN 5.2
algorithms
- Apriori 8.8
- attribute importance 8.7
- Automated Machine Learning 9.1
- automatically selecting 9.5
- Automatic Data Preparation 8.5
- Decision Tree 8.9
- Expectation Maximization 8.10
- Explicit Semantic Analysis 8.11
- Generalized Linear Model 8.12
- k-Means 8.13
- machine learning 8.1
- Minimum Description Length 8.7
- Naive Bayes 8.14
- Neural Network 8.15
- Random Forest 8.16
- settings common to all 8.3
- Singular Value Decomposition 8.17
- Support Vector Machine 8.18
algorithm selection class 9.2
ALL_PYQ_DATASTORE_CONTENTS view 10.3.1
ALL_PYQ_DATASTORES view 10.3.2
ALL_PYQ_SCRIPTS view 10.3.3
anomaly detection models 8.18
Apriori algorithm 8.8
attribute importance 8.7
Automated Machine Learning
- about 9.1
Automatic Data Preparation algorithm 8.5
Automatic Machine Learning
- connection parameter 6.2.1
Autonomous Database 6.1

C

classes
- Automated Machine Learning 9.1
- GlobalFeatureImportance 8.6
- machine learning 8.1
- oml.ai 8.7
- oml.ar 8.8
- oml.automl.AlgorithmSelection 9.2
- oml.automl.FeatureSelection 9.3
- oml.automl.ModelSelection 9.5
- oml.automl.ModelTuning 9.4
- oml.dt 8.5, 8.9
- oml.em 8.10
- oml.esa 8.11
- oml.glm 8.12
- oml.graphics 7.3
- oml.km 8.13
- oml.nb 8.14
- oml.nn 8.15
- oml.rf 8.16
- oml.svd 8.17
- oml.svm 8.18
classification algorithm 8.16
classification models 8.5, 8.9, 8.12, 8.14, 8.15, 8.16, 8.18
client
- installing for Linux for Autonomous Database 2
- installing for Linux on-premises 3.5.1.2
clustering models 8.10, 8.11, 8.13
conda enviroment 5.2
connection
- creating a on-premises database 6.2.3
- functions 6.2.1
control arguments 10.4.1
convert Python to SQL 1.3
creating
- proxy objects 6.3.4, 6.3.5
cx_Oracle.connect function 6.2.1
cx_Oracle package 6.2.1

D

data
- about moving 6.3.1
- exploring 7.2.1
- filtering 7.1.4
- preparing 7.1.1
- selecting 7.1.2
database
- connecting to an on-premises 6.2.3
data parallel processing 10.4.1
datastores
- about 6.4.1
- database views for 10.3.1, 10.3.2, 10.3.4
- deleting objects 6.4.6
- describing objects in 6.4.5
- getting information about 6.4.4
- granting or revoking access to 6.4.7
- loading objects from 6.4.3
- saving objects in 6.4.2
DCLI
- Exadata 4.2.2
- python 4.2
Decision Tree algorithm 8.9
Distributed Command Line Interface 4.2
Download environment from object storage 5.3
dropping
- tables 6.3.5

E

Embedded Python Execution
- about 10.4.1
- about the SQL interface for 10.5.1
- SQL interface for 10.5, 10.6
EM model 8.10
ESA model 8.11
Exadata 4.1
- compute nodes 4.2
- DCLI 4.2.2
Expectation Maximization algorithm 8.10
explainability 8.6
Explicit Semantic Analysis algorithm 8.11
exporting models 8.4

F

feature extraction algorithm 8.11
feature extraction class 8.17
feature selection class 9.3
function
- pyqGrant 10.5.6, 10.6.2.7
functions
- cx_Oracle.connect 6.2.1
- Embedded Python Execution 10.4.1
- for graphics 7.3
- for managing user-defined Python functions 10.4.7.1
- oml.boxplot 7.3
- oml.check_embed 6.2.1, 6.2.3
- oml.connect 6.2.1, 6.2.3
- oml.create 6.3.5
- oml.cursor 6.3.1, 6.3.5
- oml.dir 6.3.1, 6.3.4
- oml.disconnect 6.2.1, 6.2.3
- oml.do_eval 10.4.2
- oml.drop 6.3.5
- oml.ds.delete 6.4.6
- oml.ds.describe 6.4.5
- oml.ds.dir 6.4.4
- oml.ds.load 6.4.3
- oml.ds.save 6.4.2
- oml.grant 6.4.7
- oml.group_apply 10.4.4
- oml.hist 7.3
- oml.index_apply 10.4.6
- oml.isconnected 6.2.1, 6.2.3
- oml.row_apply 10.4.5
- oml.script.create 10.4.7.2
- oml.script.dir 10.4.7.3
- oml.script.drop 10.4.7.5
- oml.script.load 10.4.7.4
- oml.set_connection 6.2.1
- oml.sync 6.3.4
- oml.table_apply 10.4.3
- pyqEval 10.5.2
- pyqGroupEval 10.5.5
- pyqRowEval 10.5.4
- pyqTableEval 10.5.3

G

GLM models 8.12
granting
- access to scripts and datastores 6.4.7
- user privileges 3.4.4
graphics
- rendering 7.3

I

importing models 8.4
installing
- client for Linux for Autonomous Database 2
- client for Linux on-premises 3.5.1.2
- server for Linux on-premises 3.4.1, 3.4.2
Instant Client
- installing for Linux on-premises 3.5.1.1

K

KM model 8.13

L

libraries inOML4Py 1.4
Linux
- installing Python for 3.2
- requirements 3.1
- uninstalling on-premises client for 3.5.3
- uninstalling on-premises server for 3.4.6
Linux for Autonomous Database
- installing client for 2
Linux on-premises
- installing client for 3.5.1.2
- installing Oracle Instant Client for 3.5.1.1
- installing server for 3.4.1, 3.4.2
- supporting packages for 3.3

M

machine learning
- classes 8.1
methods
- drop 7.1.4
- drop_duplicates 7.1.4
- dropna 7.1.4
- for exploring data 7.2.1
- for preparing data 7.1.1
- pull 6.3.3
Minimum Description Length algorithm 8.7
models
- association rules 8.8
- attribute importance 8.7
- Decision Tree 8.5, 8.9
- Expectation Maximization 8.10
- explainability 8.6
- Explicit Semantic Analysis 8.11
- exporting and importing 8.4
- for anomaly detection 8.18
- for classification 8.5, 8.9, 8.12, 8.14, 8.15, 8.16, 8.18
- for clustering 8.10, 8.13
- for feature extraction 8.11, 8.17
- for regression 8.12, 8.15, 8.18
- Generalized Linear Model 8.12
- k-Means 8.13
- Naive Bayes 8.14
- Neural Network 8.15
- parametric 8.12
- persisting 8.1
- Random Forest 8.16
- Singular Value Decomposition 8.17
- Support Vector Machine 8.18
model selection 9.5
model tuning 9.4
moving data
- about 6.3.1
- to a local Python session 6.3.3
- to the database 6.3.2

N

Naive Bayes model 8.14
Neural Network model 8.15

O

oml_input_type argument 10.4.1
oml_na_omit argument 10.4.1
oml.ai class 8.7
oml.ar class 8.8
oml.automl.AlgorithmSelection class 9.2
oml.automl.FeatureSelection class 9.3
oml.automl.ModelSelection class 9.5
oml.automl.ModelTuning class 9.4
oml.boxplot function 7.3
oml.check_embed function 6.2.1, 6.2.3
oml.connect function 6.2.1, 6.2.3
oml.create function 6.3.5
oml.cursor function 6.3.1, 6.3.5
oml.dir function 6.3.1, 6.3.4
oml.disconnect function 6.2.1, 6.2.3
oml.do_eval function 10.4.2
oml.drop function 6.3.5
oml.ds.delete function 6.4.6
oml.ds.describe function 6.4.5
oml.ds.dir function 6.4.4
oml.ds.load function 6.4.3
oml.ds.save function 6.4.2
oml.dt class 8.5, 8.9
oml.em class 8.10
oml.esa class 8.11
oml.glm class 8.12
oml.grant function 6.4.7
oml.graphics class 7.3
oml.group_apply function 10.4.4
oml.hist function 7.3
oml.index_apply function 10.4.6
oml.isconnected function 6.2.1, 6.2.3
oml.km class 8.13
oml.nb class 8.14
oml.nn class 8.15
oml.push function 6.3.2
oml.revoke function 6.4.7
oml.rf class 8.16
oml.row_apply function 10.4.5
oml.script.create function 10.4.7.2
oml.script.dir function 10.4.7.3
oml.script.drop function 10.4.7.5
oml.script.load function 10.4.7.4
oml.set_connection function 6.2.1, 6.2.3
oml.svd class 8.17
oml.svm class 8.18
oml.sync function 6.3.4
oml.table_apply function 10.4.3
OML4Py 1, 4.1
- Exadata 4.2.2
on-premises client
- installing 3.5
- uninstalling 3.5.3
on-premises server
- installing 3.4
- uninstalling 3.4.6
on-premises system requirements 3.1
Oracle Machine Learning Notebooks 6.1
Oracle Machine Learning Python interpreter 6.1
Oracle wallets
- about 6.2.2

P

packages
- supporting for Linux on-premises 3.3
parallel processing 10.4.1
parametric models 8.12
PL/SQL procedures
- sys.pyqScriptCreate 10.5.8
- sys.pyqScriptDrop 10.5.9
predict.proba method 8.14
predict method 8.14
privileges
- required 3.4.4
proxy objects 1.3
- for database tables 6.3.4, 6.3.5
- storing 6.4.1
pull method 6.3.3
PYQADMIN role 3.4.4
pyqEval function 10.5.2
pyqGrant function 10.5.6, 10.6.2.7
pyqGroupEval function 10.5.5
pyqRowEval function 10.5.4
pyqTableEval function 10.5.3
pyquser.sql script 3.4.5
Python 4.1
- installing for Linux 3.2
- libraries in OML4Py 1.4
- requirements 3.1
- version used 1.4
Python interpreter 6.1
Python objects
- storing 6.4.1
python packages 5.2
Python to SQL conversion 1.3

R

Random Forest algorithm 8.16
ranking
- attribute importance 8.7
read privilege
- granting or revoking 6.4.7
regression models 8.12, 8.15
requirements
- on-premises system 3.1
resources
- managing 11
revoking
- access to scripts and datastores 6.4.7
roles
- PYQADMIN 3.4.4

S

scoring new data 1.2, 8.1
script repository
- granting or revoking access to 6.4.7
- managing user-defined Python functions in 10.4.7.1
- registering a user-defined function 10.4.7.1
scripts
- pyquser 3.4.5
server
- installing for Linux on-premises 3.4.1, 3.4.2
settings
- about model 8.2
- Apriori algorithm 8.8
- association rules 8.8
- Automatic data preparation algorithm 8.5
- Decision Tree algorithm 8.9
- Expectation Maximization model 8.10
- Explicit Semantic Analysis algorithm 8.11
- Generalized Linear Model algorithm 8.12
- k-Means algorithm 8.13
- Minimum Description Length algorithm 8.7
- Naive Bayes algorithm 8.14
- Neural Network algorithm 8.15
- Random Forest algorithm 8.16
- shared algorithm 8.3
- Singular Value Decomposition algorithm 8.17
- sttribute importance 8.7
- Support Vector Machine algorithm 8.18
special control arguments 10.4.1
SQL APIs
- pyqEval function 10.5.2
- pyqGrant function 10.5.6, 10.6.2.7
- pyqGroupEval function 10.5.5
- pyqRowEval function 10.5.4
- pyqTableEval function 10.5.3
SQL to Python conversion 1.3
supporting packages
- for Linux on-premises 3.3
SVD model 8.17
SVM models 8.18
synchronizing database tables 6.3.4
sys.pyqScriptCreate procedure 10.5.8
sys.pyqScriptDrop procedure 10.5.9

T

tables
- creating 6.3.5
- dropping 6.3.4, 6.3.5
- proxy objects for 6.3.4, 6.3.5
task parallel processing 10.4.1
transparency layer 1.3

U

uninstalling
- on-premises client 3.5.3
- on-premises server 3.4.6
USER_PYQ_DATASTORES view 10.3.4
USER_PYQ_SCRIPTS view 10.3.5
user-defined Python functions
- Embedded Python Execution of 10.4.1
users
- creating new 3.4.5

V

views
- ALL_PYQ_DATASTORE_CONTENTS 10.3.1
- ALL_PYQ_DATASTORES 10.3.2
- ALL_PYQ_SCRIPTS 10.3.3
- USER_PYQ_DATASTORES 10.3.4
- USER_PYQ_SCRIPTS 10.3.5

W

wallets
- about Oracle 6.2.2