Index

Numerics

3rd party package 7.3
3rd party packages 7.2

A

ADMIN 7.2
algorithms
- Apriori 10.8
- attribute importance 10.7
- Automated Machine Learning 11.1
- automatically selecting 11.5
- Automatic Data Preparation 10.5
- Decision Tree 10.9
- Expectation Maximization 10.10
- Explicit Semantic Analysis 10.11
- Exponential Smoothing 10.21
- Generalized Linear Model 10.12
- k-Means 10.13
- machine learning 10.1
- Minimum Description Length 10.7
- Naive Bayes 10.14
- Neural Network 10.15
- Non-Negative Matrix Factorization 10.20
- ONNX 10.16
- Random Forest 10.17
- settings common to all 10.3
- Singular Value Decomposition 10.18
- Support Vector Machine 10.19
- XGBoost 10.22
algorithm selection class 11.2
ALL_PYQ_DATASTORE_CONTENTS view 14.3.1
ALL_PYQ_DATASTORES view 14.3.2
ALL_PYQ_SCRIPTS view 14.4.1
anomaly detection models 10.19
Apriori algorithm 10.8
attribute importance 10.7
Automated Machine Learning
- about 11.1
Automatic Data Preparation algorithm 10.5
Automatic Machine Learning
- connection parameter 8.2.1
auto model search
- automated model search 1.4
- automatic model search 1.4
Autonomous Database 8.1

C

classes
- Automated Machine Learning 11.1
- GlobalFeatureImportance 10.6
- machine learning 10.1
- oml.ai 10.7
- oml.ar 10.8
- oml.automl.AlgorithmSelection 11.2
- oml.automl.FeatureSelection 11.3
- oml.automl.ModelSelection 11.5
- oml.automl.ModelTuning 11.4
- oml.dt 10.5, 10.9
- oml.em 10.10
- oml.esa 10.11
- oml.esm 10.21
- oml.glm 10.12
- oml.graphics 9.3
- oml.km 10.13
- oml.nb 10.14
- oml.nn 10.15
- oml.rf 10.17
- oml.svd 10.18
- oml.svm 10.19
- oml.xgb 10.22
- onnx 10.16
classification algorithm 10.17
classification and regression algorithm 10.22
classification and regression models 10.22
classification models 10.5, 10.9, 10.12, 10.14, 10.15, 10.17, 10.19
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 10.21
clustering models 10.10, 10.11, 10.13
Clustering models 10.21
conda enviroment 7.2
connection
- creating a on-premises database 8.2.3
- functions 8.2.1
control arguments 14.5.1
convert Python to SQL 2.3
creating
- proxy objects 8.3.4, 8.3.5
cx_Oracle.connect function 8.2.1
cx_Oracle package 8.2.1

D

data
- about moving 8.3.1
- exploring 9.2.1
- filtering 9.1.4
- preparing 9.1.1
- selecting 9.1.2
database
- connecting to an on-premises 8.2.3
Data lineage
- build_source
  - query used for build data 1.4
data parallel processing 14.5.1
datastores
- about 8.4.1
- database views for 14.3.1, 14.3.2, 14.3.3
- deleting objects 8.4.6
- describing objects in 8.4.5
- getting information about 8.4.4
- granting or revoking access to 8.4.7
- loading objects from 8.4.3
- saving objects in 8.4.2
Date Types 9.2.7
DCLI
- Exadata 6.2.2
- python 6.2
Decision Tree algorithm 10.9
Distributed Command Line Interface 6.2
doc2vec 1.4
Download environment from object storage 7.3
dropping
- tables 8.3.5

E

Embedded Python Execution
- about 14.5.1
- about the SQL interface for 14.6.1
- SQL interface for 14.6, 14.7
EM model 10.10
ESA embeddings 1.4
ESA model 10.11
Exadata 6.1
- compute nodes 6.2
- DCLI 6.2.2
Expectation Maximization 1.4
Expectation Maximization algorithm 10.10
explainability 10.6
Explicit Semantic Analysis algorithm 10.11
Exponential Smoothing Model 1.4, 10.21
export
- OML4Py function 14.5.7.6
- script function 14.5.7.6
- User defined function 14.5.7.6
exporting models 10.4

F

feature extraction algorithm 10.11
feature extraction class 10.18
feature selection class 11.3
function
- pyqGrant 14.6.6, 14.7.2.9
functions
- cx_Oracle.connect 8.2.1
- Embedded Python Execution 14.5.1
- for graphics 9.3
- for managing user-defined Python functions 14.5.7.1
- oml.check_embed 8.2.1, 8.2.3
- oml.connect 8.2.1, 8.2.3
- oml.create 8.3.5
- oml.cursor 8.3.1, 8.3.5
- oml.dir 8.3.1, 8.3.4
- oml.disconnect 8.2.1, 8.2.3
- oml.do_eval 14.5.2
- oml.drop 8.3.5
- oml.ds.delete 8.4.6
- oml.ds.describe 8.4.5
- oml.ds.dir 8.4.4
- oml.ds.load 8.4.3
- oml.ds.save 8.4.2
- oml.grant 8.4.7
- oml.graphics.boxplot 9.3
- oml.graphics.hist 9.3
- oml.group_apply 14.5.4
- oml.index_apply 14.5.6
- oml.isconnected 8.2.1, 8.2.3
- oml.row_apply 14.5.5
- oml.script.create 14.5.7.2
- oml.script.dir 14.5.7.3
- oml.script.drop 14.5.7.5
- oml.script.load 14.5.7.4
- oml.set_connection 8.2.1
- oml.sync 8.3.4
- oml.table_apply 14.5.3
- pyqEval 14.6.2
- pyqGroupEval 14.6.5
- pyqRowEval 14.6.4
- pyqTableEval 14.6.3

G

GLM link functions 1.4
GLM models 10.12
granting
- access to scripts and datastores 8.4.7
- user privileges 4.2.1.3.3
graphics
- rendering 9.3

I

import
- OML4Py function 14.5.7.7
- script function 14.5.7.7
- User defined function 14.5.7.7
importing models 10.4
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 10.13

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 10.1
methods
- drop 9.1.4
- drop_duplicates 9.1.4
- dropna 9.1.4
- for exploring data 9.2.1
- for preparing data 9.1.1
- pull 8.3.3
Minimum Description Length algorithm 10.7
models
- association rules 10.8
- attribute importance 10.7
- Clustering 10.21
- Decision Tree 10.5, 10.9
- Expectation Maximization 10.10
- explainability 10.6
- Explicit Semantic Analysis 10.11
- exporting and importing 10.4
- for anomaly detection 10.19
- for classification 10.5, 10.9, 10.12, 10.14, 10.15, 10.17, 10.19
- for classification and regression 10.22
- for clustering 10.10, 10.13
- for Clustering 10.21
- for feature extraction 10.11, 10.18
- for regression 10.12, 10.15, 10.19
- Generalized Linear Model 10.12
- k-Means 10.13
- Naive Bayes 10.14
- Neural Network 10.15
- Non-Negative Matrix Factorization 10.20
- parametric 10.12
- persisting 10.1
- Random Forest 10.17
- Singular Value Decomposition 10.18
- Support Vector Machine 10.19
- XGBoost 10.22
model selection 11.5
model tuning 11.4
moving data
- about 8.3.1
- to a local Python session 8.3.3
- to the database 8.3.2

N

Naive Bayes model 10.14
Neural Network model 10.15
NMF models 10.20

O

ODMS_BOXCOX 1.4
oml_input_type argument 14.5.1
oml_na_omit argument 14.5.1
oml.ai class 10.7
oml.ar class 10.8
oml.automl.AlgorithmSelection class 11.2
oml.automl.FeatureSelection class 11.3
oml.automl.ModelSelection class 11.5
oml.automl.ModelTuning class 11.4
oml.check_embed function 8.2.1, 8.2.3
oml.connect function 8.2.1, 8.2.3
oml.create function 8.3.5
oml.cursor function 8.3.1, 8.3.5
oml.Datetime 9.2.7
oml.dir function 8.3.1, 8.3.4
oml.disconnect function 8.2.1, 8.2.3
oml.do_eval function 14.5.2
oml.drop function 8.3.5
oml.ds.delete function 8.4.6
oml.ds.describe function 8.4.5
oml.ds.dir function 8.4.4
oml.ds.load function 8.4.3
oml.ds.save function 8.4.2
oml.dt class 10.5, 10.9
oml.em class 10.10
oml.esa class 10.11
oml.esm class 10.21
oml.glm class 10.12
oml.grant function 8.4.7
oml.graphics.boxplot function 9.3
oml.graphics.hist function 9.3
oml.graphics class 9.3
oml.group_apply function 14.5.4
oml.index_apply function 14.5.6
oml.Integer 9.2.7
oml.isconnected function 8.2.1, 8.2.3
oml.km class 10.13
oml.nb class 10.14
oml.nn class 10.15
oml.onnx class 10.16
oml.push function 8.3.2
oml.revoke function 8.4.7
oml.rf class 10.17
oml.row_apply function 14.5.5
oml.script.create function 14.5.7.2
oml.script.dir function 14.5.7.3
oml.script.drop function 14.5.7.5
oml.script.load function 14.5.7.4
oml.set_connection function 8.2.1, 8.2.3
oml.svd class 10.18
oml.svm class 10.19
oml.sync function 8.3.4
oml.table_apply function 14.5.3
oml.Timedelta 9.2.7
oml.Timezone 9.2.7
oml.xgb class 10.22
OML4Py 2, 6.1
- Exadata 6.2.2
ONNX 12.9
ONNX algorithm 10.16
ONNX models 12.9
ONNX model size 12.9
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 8.1
Oracle Machine Learning Python interpreter 8.1
Oracle wallets
- about 8.2.2
ore.nmf function 10.20

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 14.5.1
parametric models 10.12
PL/SQL procedures
- sys.pyqScriptCreate 14.6.8
- sys.pyqScriptDrop 14.6.9
predict.proba method 10.14
predict method 10.14
privileges
- required 4.2.1.3.3
proxy objects 2.3
- for database tables 8.3.4, 8.3.5
- storing 8.4.1
pull method 8.3.3
PYQADMIN role 4.2.1.3.3
pyqEval function 14.6.2
pyqGrant function 14.6.6, 14.7.2.9
pyqGroupEval function 14.6.5
pyqRowEval function 14.6.4
pyqTableEval function 14.6.3
pyquser.sql script 4.2.1.3.4
Python 6.1
- 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 8.1
Python objects
- storing 8.4.1
python packages 7.2
Python to SQL conversion 2.3

R

Random Forest algorithm 10.17
ranking
- attribute importance 10.7
read privilege
- granting or revoking 8.4.7
regression models 10.12, 10.15
resources
- managing 15
revoking
- access to scripts and datastores 8.4.7
roles
- PYQADMIN 4.2.1.3.3

S

scoring new data 2.2, 10.1
script repository
- granting or revoking access to 8.4.7
- managing user-defined Python functions in 14.5.7.1
- registering a user-defined function 14.5.7.1
scripts
- pyquser 4.2.1.3.4
server
- installing for Linux on-premises 4.2.1.3.1
setting
- onnx 10.16
settings
- about model 10.2
- Apriori algorithm 10.8
- association rules 10.8
- Automatic data preparation algorithm 10.5
- Decision Tree algorithm 10.9
- Expectation Maximization model 10.10
- Explicit Semantic Analysis algorithm 10.11
- Exponential Smoothing Model 10.21
- Generalized Linear Model algorithm 10.12
- k-Means algorithm 10.13
- Minimum Description Length algorithm 10.7
- Naive Bayes algorithm 10.14
- Neural Network algorithm 10.15
- Random Forest algorithm 10.17
- shared algorithm 10.3
- Singular Value Decomposition algorithm 10.18
- sttribute importance 10.7
- Support Vector Machine algorithm 10.19
- XGBoost algorithm 10.22
special control arguments 14.5.1
SQL APIs
- pyqEval function 14.6.2
- pyqGrant function 14.6.6, 14.7.2.9
- pyqGroupEval function 14.6.5
- pyqRowEval function 14.6.4
- pyqTableEval function 14.6.3
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 10.18
SVM models 10.19
synchronizing database tables 8.3.4
sys.pyqScriptCreate procedure 14.6.8
sys.pyqScriptDrop procedure 14.6.9

T

tables
- creating 8.3.5
- dropping 8.3.4, 8.3.5
- proxy objects for 8.3.4, 8.3.5
task parallel processing 14.5.1
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 14.3.3
USER_PYQ_SCRIPTS view 14.4.2
user-defined Python functions
- Embedded Python Execution of 14.5.1
users
- creating new 4.2.1.3.4

V

views
- ALL_PYQ_DATASTORE_CONTENTS 14.3.1
- ALL_PYQ_DATASTORES 14.3.2
- ALL_PYQ_SCRIPTS 14.4.1
- USER_PYQ_DATASTORES 14.3.3
- USER_PYQ_SCRIPTS 14.4.2

W

wallets
- about Oracle 8.2.2
WINSORIZE 1.4

X

XGBoost algorithm 10.22
XGBoost constraints 1.4
XGBoost survival analysis 1.4