Choose powerful tools to create and view predictive models
SPSS Modeler is a powerful predictive analytics platform that is designed to bring predictive intelligence to decisions made by individuals, groups, systems and your enterprise. SPSS Modeler scales from desktop deployments to integration with operational systems to provide you with a range of advanced algorithms and techniques. Applying these techniques to decisions can result in rapid ROI and can
enable organizations to proactively and repeatedly reduce costs while increasing productivity.:
A range of models
SPSS Modeler offers an array of modeling techniques, including all of the following
Make predictions or forecasts based on historical data with techniques. Examples include decision trees, neural networks,
logistic regression, support vector machines, Cox regression, generalized linear mixed models (GLMM) and more. Use automatic classification modeling for both binary and numeric outcomes to streamline model
creation or Self-Learning Response
Modeling (SLRM) to build a model that you
can continually update or re-estimate without having to rebuild the model.
Group people or detect unusual patterns
with automatic clustering, anomaly detection and clustering neural network techniques.
Use automatic classification to apply multiple algorithms with a single step and take the guesswork out of selecting the right technique.
Association algorithms. Discover associations, links or sequences with Apriori, CARMA and sequential association.
Time series and forecasting. Generate forecasts for one or more series over time with statistical modeling techniques. Using temporal causal modeling, you can discover causal relationships among a large number of series.
Extendibility with R programming language.
Apply transformations, use scripts to analyze, summarize or produce text and graphical output with R. With the Custom Dialog Builder, you can share and reuse R code with those who choose not to use programming for analysis.
Monte Carlo simulation. Account for uncertainty in inputs to predictive models. Model uncertain inputs based on historical data or with probability distributions to generate simulated values, and then use them in the predictive model to generate an outcome. The process can be repeated thousands or tens of thousands of times. The result is a distribution of outcomes that can provide answers to uestions that are based on realistically generated data.
Data preparation and manipulation
SPSS Modeler offers a variety of ways to manipulate and prepare data for analysis at the record or field (or variable) level. Among the methods used to help make sure you data is
in the best format for the specific type of analysis that is being undertaken are:
Select, Sample and Distinct nodes enable
you to choose specific rows of data. You can merge and append nodes to join data by adding columns or rows to a dataset. Aggregate and Recency, Frequency,
Monetary (RFM) Aggregate nodes summarize records to a single row. A Balance node adjusts the proportions of records in imbalanced data and a Sort node reorders based on value. The Space Time Box node creates geospatial and time-based data for records.
Field operations. A Type node specifies metadata and properties of a dataset, and the Filter node discards fields. The Derive node creates new fields and a Filler node can replace existing field values. Data can be restructured with the Set to Flag, Restructure or Transpose nodes and regrouped with the Reclassify or Binning nodes. To assist with modeling, the Partition node can split the data and the History node and Time Intervals nodes can create additional fields. The Field Reorder node defines the display ordering to make certain fields easier to view.
Automated data modeling
With the automated modeling features of SPSS Modeler, nonanalysts can produce accurate models quickly without specialized skills. In addition, advanced predictive modeling
capabilities enable professional analysts to create the most sophisticated of streams.
Automated modeling enables you to compare multiple modeling approaches. By setting specific options for each model type (or using the defaults), you can explore a multitude
of model combinations and options. The generated models are then ranked based on the measure specified, saving the best for
use in scoring or further analysis.