SPSS Modeler offers modeling algorithms that are taken
from machine learning, artificial intelligence, and statistics. You can use these modeling
algorithms to analyze your data and gain new business insights. With SPSS Modeler, you can quickly develop predictive models and deploy them into
business operations.
What is SPSS Modeler?
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SPSS Modeler is a data mining application, where you can build data mining
SPSS Modeler flows by using the visual interface. Programming is not
required. You can build SPSS Modeler flows to explore your data,
model outcomes, try different models, and investigate relationships to find useful information.
SPSS Modeler uses the Cross-Industry Standard Process for Data Mining
(CRISP-DM) methodology, which is an industry-proven way to guide your data mining
efforts.
Using the Flow Editor, you prepare or shape
data, train or deploy a model, or transform data and export it back to a database table or a file in
Cloud Object Storage. Running an SPSS Modeler
flow consumes capacity unit hours. For more information, see Watson Studio environments compute
usage.
You can use the advanced analytics in SPSS Modeler to discover patterns in your data and tune models. You can then
deploy these models in your business to make predictions on new data with unknown outcomes. The
models can systemically analyze data and find business insights and opportunities. If you have
access to the watsonx.ai Runtime service, you can promote
models to deployment spaces to run them.
Data formats
Relational: Tables in relational data sources
Tabular: Tables in data files such as .xls, .csv,
.json, or .sas. For Excel files, only the first sheet is
read.
Textual: In the supported relational tables or files
Choose from over 40 modeling algorithms, and many other nodes
Use automatic modeling functions
Model time series or geospatial data
Classify textual data
Identify relationships between the concepts in textual data
Note:Watson Studio doesn't include SPSS Modeler functionality in Peru, Ecuador, Colombia, or
Venezuela.
Scripting
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You can use scripting in SPSS Modeler to automate tasks that are
highly repetitive or time consuming to perform manually. Scripts can perform all the same types of
actions as users with a mouse or a keyboard, and you can write scripts in R, Python, or Python for
Spark.
The following are some of the tasks that you can automate with scripts:
Impose a specific order for running nodes in a flow
Set properties for a node
Set up a process that automatically takes a model training flow, runs it, and produces the
corresponding model-testing flow.
Related services
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If you have access to other services on Cloud Pak for Data,
you can use them with SPSS Modeler. The following services offer
features that complement work in SPSS Modeler.
Note:Watson.ai
Studio is prerequisite service for SPSS Modeler.
Quickly build, run, and manage generative AI and machine learning applications with built-in
performance and scalability.
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Cloud Pak for Data relationship map
Use this interactive map to learn about the relationships between your tasks, the tools you need, the services that provide the tools, and where you use the tools.
Select any task, tool, service, or workspace
You'll learn what you need, how to get it, and where to use it.
Some tools perform the same tasks but have different features and levels of automation.
Jupyter notebook editor
Prepare data
Visualize data
Build models
Deploy assets
Create a notebook in which you run Python, R, or Scala code to prepare, visualize, and analyze data, or build a model.
AutoAI
Build models
Automatically analyze your tabular data and generate candidate model pipelines customized for your predictive modeling problem.
SPSS Modeler
Prepare data
Visualize data
Build models
Create a visual flow that uses modeling algorithms to prepare data and build and train a model, using a guided approach to machine learning that doesn’t require coding.
Decision Optimization
Build models
Visualize data
Deploy assets
Create and manage scenarios to find the best solution to your optimization problem by comparing different combinations of your model, data, and solutions.
Data Refinery
Prepare data
Visualize data
Create a flow of ordered operations to cleanse and shape data. Visualize data to identify problems and discover insights.
Orchestration Pipelines
Prepare data
Build models
Deploy assets
Automate the model lifecycle, including preparing data, training models, and creating deployments.
RStudio
Prepare data
Build models
Deploy assets
Work with R notebooks and scripts in an integrated development environment.
Federated learning
Build models
Create a federated learning experiment to train a common model on a set of remote data sources. Share training results without sharing data.
Deployments
Deploy assets
Monitor models
Deploy and run your data science and AI solutions in a test or production environment.
Catalogs
Catalog data
Governance
Find and share your data and other assets.
Metadata import
Prepare data
Catalog data
Governance
Import asset metadata from a connection into a project or a catalog.
Metadata enrichment
Prepare data
Catalog data
Governance
Enrich imported asset metadata with business context, data profiling, and quality assessment.
Data quality rules
Prepare data
Governance
Measure and monitor the quality of your data.
Masking flow
Prepare data
Create and run masking flows to prepare copies of data assets that are masked by advanced data protection rules.
Governance
Governance
Create your business vocabulary to enrich assets and rules to protect data.
Data lineage
Governance
Track data movement and usage for transparency and determining data accuracy.
AI factsheet
Governance
Monitor models
Track AI models from request to production.
DataStage flow
Prepare data
Create a flow with a set of connectors and stages to transform and integrate data. Provide enriched and tailored information for your enterprise.
Data virtualization
Prepare data
Create a virtual table to segment or combine data from one or more tables.
OpenScale
Monitor models
Measure outcomes from your AI models and help ensure the fairness, explainability, and compliance of all your models.
Data replication
Prepare data
Replicate data to target systems with low latency, transactional integrity and optimized data capture.
Master data
Prepare data
Consolidate data from the disparate sources that fuel your business and establish a single, trusted, 360-degree view of your customers.
Services you can use
Services add features and tools to the platform.
watsonx.ai Studio
Develop powerful AI solutions with an integrated collaborative studio and industry-standard APIs and SDKs. Formerly known as Watson Studio.
watsonx.ai Runtime
Quickly build, run and manage generative AI and machine learning applications with built-in performance and scalability. Formerly known as Watson Machine Learning.
IBM Knowledge Catalog
Discover, profile, catalog, and share trusted data in your organization.
DataStage
Create ETL and data pipeline services for real-time, micro-batch, and batch data orchestration.
Data Virtualization
View, access, manipulate, and analyze your data without moving it.
Watson OpenScale
Monitor your AI models for bias, fairness, and trust with added transparency on how your AI models make decisions.
Data Replication
Provide efficient change data capture and near real-time data delivery with transactional integrity.
Match360 with Watson
Improve trust in AI pipelines by identifying duplicate records and providing reliable data about your customers, suppliers, or partners.
Manta Data Lineage
Increase data pipeline transparency so you can determine data accuracy throughout your models and systems.
Where you'll work
Collaborative workspaces contain tools for specific tasks.
Project
Where you work with data.
> Projects > View all projects
Catalog
Where you find and share assets.
> Catalogs > View all catalogs
Space
Where you deploy and run assets that are ready for testing or production.
> Deployments
Categories
Where you manage governance artifacts.
> Governance > Categories
Data virtualization
Where you virtualize data.
> Data > Data virtualization
Master data
Where you consolidate data into a 360 degree view.