0.13.0
Home
Installation
How to use this guide
Getting started
Support
User Guide
Tutorials
getML suite
Importing data
Annotating data
Data model
Feature engineering
Predicting
Hyperparameter optimization
Deployment
API Documentation
Python API
Command line interface
About
getML
Docs
»
User Guide
User Guide
ΒΆ
The getML suite
Engine
Starting the engine
Shutting down the engine
Logging
Managing data using projects
Monitor
Projects
Pipelines
Top-level view
Fitted pipelines
Score plots
Deployed pipelines
Pipeline View
ROC curve
Accuracy
Correlations and Importances
Features table
Hyperparameters
Data model
Feature View
Density plot
Average target plot
Data Frames
Top-level view
Data frame view
Column view
Density plot
Relation plot
Database
Table View
Log
Docs
Configuration
Monitor configuration
Engine configuration
Certificates
User Management
Support
Logout
Shutdown
Python API
Connecting to the getML engine
Session management
Lifecycles and synchronization between engine and API
Lifecycle of a
DataFrame
Lifecycle of a
Pipeline
Importing data
Unified import interface
CSV interface
Import from CSV
Export to CSV
Pandas interface
Import from Pandas
Export to Pandas
JSON interface
Import from JSON
Export to JSON
SQLite3 interface
Import from SQLite3
Export to SQLite3
MySQL interface
Import from MySQL
Export to MySQL
MariaDB interface
Import from MariaDB
Export to MariaDB
PostgreSQL interface
Import from PostgreSQL
Export to PostgreSQL
Greenplum interface
Import from Greenplum
Export to Greenplum
ODBC interface
An example: Microsoft SQL Server
Important: Always pass
escape_chars
Import data using ODBC
Export data using ODBC
Data Frames
Handling of NULL values
Annotating data
In short
Roles
A note on reproducibility and efficiency
Join key
Time stamp
Target
Numerical
Categorical
Unused_float
Unused_string
Units
Data model
Tables
The population table
Peripheral tables
Placeholders
Joins
Data schemata
The star schema
The snowflake schema
Time series
Self-joining a single table
Features based on time stamps
Feature engineering
Definition
Design principles
Algorithms
Multirel
Relboost
Best practices
How to make features more interpretable
How to add handcrafted features
Which hyperparameters have the most impact for Multirel?
How to reduce the training time of Multirel
Influence of the data set on the training time
Predicting
Using getML
Using external software
Hyperparameter optimization
Random search
Latin hypercube search
Gaussian hyperparameter search
Deployment
Prerequisites
HTTP(S) Endpoints
Request Format
Time stamp formats in requests
Using an existing
DataFrame
Using data from a database
Transform Endpoint
Predict Endpoint