Index

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 9.21
- 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 9.20
- ONNX 9.16
- Random Forest 9.17
- settings common to all 9.3
- Singular Value Decomposition 9.18
- Support Vector Machine 9.19
- XGBoost 9.22
algorithm selection class 10.2
ALL_PYQ_DATASTORE_CONTENTS view 13.3.1
ALL_PYQ_DATASTORES view 13.3.2
ALL_PYQ_SCRIPTS view 13.4.1
anomaly detection models 9.19
Apriori algorithm 9.8
attribute importance 9.7
Automated Machine Learning
- about 10.1
Automatic Data Preparation algorithm 9.5
Automatic Machine Learning
- connection parameter 7.2.1
auto model search
- automated model search 1.4
- automatic model search 1.4
Autonomous Database 7.1

C

classes
- Automated Machine Learning 10.1
- GlobalFeatureImportance 9.6
- machine learning 9.1
- oml.ai 9.7
- oml.ar 9.8
- oml.automl.AlgorithmSelection 10.2
- oml.automl.FeatureSelection 10.3
- oml.automl.ModelSelection 10.5
- oml.automl.ModelTuning 10.4
- oml.dt 9.5, 9.9
- oml.em 9.10
- oml.esa 9.11
- oml.esm 9.21
- oml.glm 9.12
- oml.graphics 8.3
- oml.km 9.13
- oml.nb 9.14
- oml.nn 9.15
- oml.rf 9.17
- oml.svd 9.18
- oml.svm 9.19
- oml.xgb 9.22
- onnx 9.16
classification algorithm 9.17
classification and regression algorithm 9.22
classification and regression models 9.22
classification models 9.5, 9.9, 9.12, 9.14, 9.15, 9.17, 9.19
client
- installing for Linux for Autonomous Database 3
- installing for Linux on-premises 4.2.1.4.1.2, 4.2.3.4.1.2, 4.2.4.4.1.2
Clustering algorithm 9.21
clustering models 9.10, 9.11, 9.13
Clustering models 9.21
conda enviroment 6.2
connection
- creating a on-premises database 7.2.3
- functions 7.2.1
control arguments 13.5.1
convert Python to SQL 2.3
creating
- proxy objects 7.3.4, 7.3.5
cx_Oracle.connect function 7.2.1
cx_Oracle package 7.2.1

D

data
- about moving 7.3.1
- exploring 8.2.1
- filtering 8.1.4
- preparing 8.1.1
- selecting 8.1.2
database
- connecting to an on-premises 7.2.3
Data lineage
- build_source
  - query used for build data 1.4
data parallel processing 13.5.1
datastores
- about 7.4.1
- database views for 13.3.1, 13.3.2, 13.3.3
- deleting objects 7.4.6
- describing objects in 7.4.5
- getting information about 7.4.4
- granting or revoking access to 7.4.7
- loading objects from 7.4.3
- saving objects in 7.4.2
Date Types 8.2.7
DCLI
- Exadata 5.2.2
- python 5.2
Decision Tree algorithm 9.9
Distributed Command Line Interface 5.2
doc2vec 1.4
Download environment from object storage 6.3
dropping
- tables 7.3.5

E

Embedded Python Execution
- about 13.5.1
- about the SQL interface for 13.6.1
- SQL interface for 13.6, 13.7
EM model 9.10
ESA embeddings 1.4
ESA model 9.11
Exadata 5.1
- compute nodes 5.2
- DCLI 5.2.2
Expectation Maximization 1.4
Expectation Maximization algorithm 9.10
explainability 9.6
Explicit Semantic Analysis algorithm 9.11
Exponential Smoothing Model 1.4, 9.21
export
- OML4Py function 13.5.7.6
- script function 13.5.7.6
- User defined function 13.5.7.6
exporting models 9.4

F

feature extraction algorithm 9.11
feature extraction class 9.18
feature selection class 10.3
function
- pyqGrant 13.6.6, 13.7.2.7
functions
- cx_Oracle.connect 7.2.1
- Embedded Python Execution 13.5.1
- for graphics 8.3
- for managing user-defined Python functions 13.5.7.1
- oml.check_embed 7.2.1, 7.2.3
- oml.connect 7.2.1, 7.2.3
- oml.create 7.3.5
- oml.cursor 7.3.1, 7.3.5
- oml.dir 7.3.1, 7.3.4
- oml.disconnect 7.2.1, 7.2.3
- oml.do_eval 13.5.2
- oml.drop 7.3.5
- oml.ds.delete 7.4.6
- oml.ds.describe 7.4.5
- oml.ds.dir 7.4.4
- oml.ds.load 7.4.3
- oml.ds.save 7.4.2
- oml.grant 7.4.7
- oml.graphics.boxplot 8.3
- oml.graphics.hist 8.3
- oml.group_apply 13.5.4
- oml.index_apply 13.5.6
- oml.isconnected 7.2.1, 7.2.3
- oml.row_apply 13.5.5
- oml.script.create 13.5.7.2
- oml.script.dir 13.5.7.3
- oml.script.drop 13.5.7.5
- oml.script.load 13.5.7.4
- oml.set_connection 7.2.1
- oml.sync 7.3.4
- oml.table_apply 13.5.3
- pyqEval 13.6.2
- pyqGroupEval 13.6.5
- pyqRowEval 13.6.4
- pyqTableEval 13.6.3

G

GLM link functions 1.4
GLM models 9.12
granting
- access to scripts and datastores 7.4.7
- user privileges 4.2.1.3.3
graphics
- rendering 8.3

I

import
- OML4Py function 13.5.7.7
- script function 13.5.7.7
- User defined function 13.5.7.7
importing models 9.4
improved data preparation 1.4
installing
- client for Linux for Autonomous Database 3
- client for Linux on-premises 4.2.1.4.1.2, 4.2.3.4.1.2, 4.2.4.4.1.2
- server for Linux on-premises 4.2.1.3.1
Instant Client
- installing for Linux on-premises 4.2.1.4.1.1, 4.2.3.4.1.1, 4.2.4.4.1.1

K

KMeans 1.4
KM model 9.13

L

libraries inOML4Py 2.4
Linux
- installing Python for 4.2.1.1, 4.2.2.1, 4.2.3.1, 4.2.4.1
- uninstalling on-premises client for 4.2.1.3.5, 4.2.2.4.4, 4.2.3.4.4, 4.2.4.4.4, 4.2.1.4.3, 4.2.2.4.3, 4.2.3.4.3, 4.2.4.4.3
- uninstalling on-premises server for 4.2.1.3.7
Linux for Autonomous Database
- installing client for 3
Linux on-premises
- installing client for 4.2.1.4.1.2, 4.2.3.4.1.2, 4.2.4.4.1.2
- installing Oracle Instant Client for 4.2.1.4.1.1, 4.2.3.4.1.1, 4.2.4.4.1.1
- installing server for 4.2.1.3.1
- supporting packages for 4.2.1.2, 4.2.2.2, 4.2.3.2, 4.2.4.2

M

machine learning
- classes 9.1
methods
- drop 8.1.4
- drop_duplicates 8.1.4
- dropna 8.1.4
- for exploring data 8.2.1
- for preparing data 8.1.1
- pull 7.3.3
Minimum Description Length algorithm 9.7
models
- association rules 9.8
- attribute importance 9.7
- Clustering 9.21
- Decision Tree 9.5, 9.9
- Expectation Maximization 9.10
- explainability 9.6
- Explicit Semantic Analysis 9.11
- exporting and importing 9.4
- for anomaly detection 9.19
- for classification 9.5, 9.9, 9.12, 9.14, 9.15, 9.17, 9.19
- for classification and regression 9.22
- for clustering 9.10, 9.13
- for Clustering 9.21
- for feature extraction 9.11, 9.18
- for regression 9.12, 9.15, 9.19
- Generalized Linear Model 9.12
- k-Means 9.13
- Naive Bayes 9.14
- Neural Network 9.15
- Non-Negative Matrix Factorization 9.20
- parametric 9.12
- persisting 9.1
- Random Forest 9.17
- Singular Value Decomposition 9.18
- Support Vector Machine 9.19
- XGBoost 9.22
model selection 10.5
model tuning 10.4
moving data
- about 7.3.1
- to a local Python session 7.3.3
- to the database 7.3.2

N

Naive Bayes model 9.14
Neural Network model 9.15
NMF models 9.20

O

ODMS_BOXCOX 1.4
oml_input_type argument 13.5.1
oml_na_omit argument 13.5.1
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.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 13.5.2
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 9.21
oml.glm class 9.12
oml.grant function 7.4.7
oml.graphics.boxplot function 8.3
oml.graphics.hist function 8.3
oml.graphics class 8.3
oml.group_apply function 13.5.4
oml.index_apply function 13.5.6
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.onnx class 9.16
oml.push function 7.3.2
oml.revoke function 7.4.7
oml.rf class 9.17
oml.row_apply function 13.5.5
oml.script.create function 13.5.7.2
oml.script.dir function 13.5.7.3
oml.script.drop function 13.5.7.5
oml.script.load function 13.5.7.4
oml.set_connection function 7.2.1, 7.2.3
oml.svd class 9.18
oml.svm class 9.19
oml.sync function 7.3.4
oml.table_apply function 13.5.3
oml.Timedelta 8.2.7
oml.Timezone 8.2.7
oml.xgb class 9.22
OML4Py 2, 5.1
- Exadata 5.2.2
ONNX 11.9
ONNX algorithm 9.16
ONNX models 11.9
ONNX model size 11.9
on-premises client
- installing 4.2.1.4, 4.2.2.4, 4.2.3.4, 4.2.4.4
- uninstalling 4.2.1.3.5, 4.2.2.4.4, 4.2.3.4.4, 4.2.4.4.4, 4.2.1.4.3, 4.2.2.4.3, 4.2.3.4.3, 4.2.4.4.3
on-premises server
- installing 4.2.1.3, 4.2.2.3
- uninstalling 4.2.1.3.7
Oracle Machine Learning Notebooks 7.1
Oracle Machine Learning Python interpreter 7.1
Oracle wallets
- about 7.2.2
ore.nmf function 9.20

P

packages
- supporting for Linux on-premises 4.2.1.2, 4.2.2.2, 4.2.3.2, 4.2.4.2
parallel processing 13.5.1
parametric models 9.12
PL/SQL procedures
- sys.pyqScriptCreate 13.6.8
- sys.pyqScriptDrop 13.6.9
predict.proba method 9.14
predict method 9.14
privileges
- required 4.2.1.3.3
proxy objects 2.3
- for database tables 7.3.4, 7.3.5
- storing 7.4.1
pull method 7.3.3
PYQADMIN role 4.2.1.3.3
pyqEval function 13.6.2
pyqGrant function 13.6.6, 13.7.2.7
pyqGroupEval function 13.6.5
pyqRowEval function 13.6.4
pyqTableEval function 13.6.3
pyquser.sql script 4.2.1.3.4
Python 5.1
- installing for Linux 4.2.1.1, 4.2.2.1, 4.2.3.1, 4.2.4.1
- libraries in OML4Py 2.4
- version used 2.4
Python interpreter 7.1
Python objects
- storing 7.4.1
python packages 6.2
Python to SQL conversion 2.3

R

Random Forest algorithm 9.17
ranking
- attribute importance 9.7
read privilege
- granting or revoking 7.4.7
regression models 9.12, 9.15
resources
- managing 14
revoking
- access to scripts and datastores 7.4.7
roles
- PYQADMIN 4.2.1.3.3

S

scoring new data 2.2, 9.1
script repository
- granting or revoking access to 7.4.7
- managing user-defined Python functions in 13.5.7.1
- registering a user-defined function 13.5.7.1
scripts
- pyquser 4.2.1.3.4
server
- installing for Linux on-premises 4.2.1.3.1
setting
- onnx 9.16
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 9.21
- 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 9.17
- shared algorithm 9.3
- Singular Value Decomposition algorithm 9.18
- sttribute importance 9.7
- Support Vector Machine algorithm 9.19
- XGBoost algorithm 9.22
special control arguments 13.5.1
SQL APIs
- pyqEval function 13.6.2
- pyqGrant function 13.6.6, 13.7.2.7
- pyqGroupEval function 13.6.5
- pyqRowEval function 13.6.4
- pyqTableEval function 13.6.3
SQL to Python conversion 2.3
supporting packages
- for Linux on-premises 4.2.1.2, 4.2.2.2, 4.2.3.2, 4.2.4.2
SVD model 9.18
SVM models 9.19
synchronizing database tables 7.3.4
sys.pyqScriptCreate procedure 13.6.8
sys.pyqScriptDrop procedure 13.6.9

T

tables
- creating 7.3.5
- dropping 7.3.4, 7.3.5
- proxy objects for 7.3.4, 7.3.5
task parallel processing 13.5.1
transparency layer 2.3

U

uninstalling
- on-premises client 4.2.1.3.5, 4.2.2.4.4, 4.2.3.4.4, 4.2.4.4.4, 4.2.1.4.3, 4.2.2.4.3, 4.2.3.4.3, 4.2.4.4.3
- on-premises server 4.2.1.3.7
USER_PYQ_DATASTORES view 13.3.3
USER_PYQ_SCRIPTS view 13.4.2
user-defined Python functions
- Embedded Python Execution of 13.5.1
users
- creating new 4.2.1.3.4

V

views
- ALL_PYQ_DATASTORE_CONTENTS 13.3.1
- ALL_PYQ_DATASTORES 13.3.2
- ALL_PYQ_SCRIPTS 13.4.1
- USER_PYQ_DATASTORES 13.3.3
- USER_PYQ_SCRIPTS 13.4.2

W

wallets
- about Oracle 7.2.2
WINSORIZE 1.4

X

XGBoost algorithm 9.22
XGBoost constraints 1.4
XGBoost survival analysis 1.4