SAS^® High-Performance Analytics

Features

High-performance analytics

• Enables high-performance capabilities of select SAS Analytics products (i.e., Base SAS®, SAS/STAT®, SAS/ETS® and SAS® Enterprise Miner™).
• Uses a platform that supports access to large data stored in nearly any format currently on the market.
• Reads input data in parallel and writes output data in parallel.
• Executes SAS Analytics procedures across a distributed compute environment in parallel.

High-performance data exploration

• Enables large-scale data exploration and summarization through a series of parallelized procedures.
• Generates descriptive statistics on a large scale, very quickly.
• Creates mean, min, max, range and measures of spread and centrality along with data for cardinality, summary and levels of variables.

High-performance variable reduction

• Reduces dimensionality by using the HPREDUCE procedure to select a subset of the original variables (variable selection) to preserve model interpretation.
• Performs unsupervised variable section by identifying a set of variables that jointly explain the maximum amount of data variance (covariance analysis).
• Provides distributed computation and output of the CORR, COV or SSCP matrix.
• Uses the CLASS statement to support categorical inputs.
• Supports main and interaction effects with the VAR statement.
• Outputs statistics and matrix information for exploratory data analysis that can also be used as direct input for statistical procedures. This saves time by eliminating redundant matrix aggregations.
  

High-performance linear regression

• Supports partitioning of data into training, validation and testing roles.
• Supports a FREQ statement for grouped analysis and a WEIGHT statement for weighted analysis.
• Provides multiple effect-selection methods.

High-performance logistic regression

• Predicts binary, binomial and multinomial outcomes.
• Provides model-building syntax with the CLASS and effect-based MODEL statements.
• Provides cumulative link models for ordinal data and generalized logistic modeling for unordered multinomial data enables model building (variable selection) through the SELECTION statement.
• Provides a WEIGHT statement for weighted analysis and a FREQ statement for grouped analysis.
• Provides an OUTPUT statement to produce a data set with predicted probabilities and other observation-wise statistics.

High-performance nonlinear regression

• Computes analytical derivatives of user-provided expressions for more robust parameter estimations, improving the estimations as well as making them faster.
• Evaluates user-provided expressions and their confidence limits with the ESTIMATE and PREDICT statements.
• Estimates parameters by using least squares and the maximum likelihood method.
  

High-performance mixed linear models

• Fits a variety of mixed linear models to data and enables you to use these fitted models to make statistical inferences about the data.
• Supports multiple covariance structures.
• Provides appropriate standard errors for all specified, estimable linear combinations of fixed and random effects, and corresponding t-tests and F-tests.
• REML and ML estimation methods are implemented with a variety of optimization algorithms.
• Provides special dense and sparse algorithms that take advantage of distributed and multiple-core computing environments.
  

High-performance neural networks

• Provides automatic standardization of input and target variables.
• Provides automatic selection and use of a validation data subset.
• Provides automatic termination of training when the validation error stops improving.
• Provides the ability to weight individual observations.

High-performance data mining

• Includes the following high-performance-enabled SAS Enterprise Miner nodes:
  • HP Data Source.
  • HP Explore.
  • HP Transform.
  • HP Variable Selection.
  • HP Regression.
  • HP Neural Network.
  • HP Random Forest experimental.
  • HP Impute.

High-performance data mining DATABASE procedure

• Creates summaries of key input data sources, including:
  • Number of observations.
  • Number of observations that contain a missing value.
  • Minimum observed value.
  • Maximum observed value.
  • Mean of observed values.
  • Standard deviation.
  • Measure of asymmetry.
  • Measure of the "heaviness of the tails."
  • Sum of all non-missing observations.
  • Corrected sum of squares.
  • Sum of squares.

High-performance random forest decision trees (experimental)

• Creates an ensemble of hundreds of decision trees to predict a single target.
• Trains hundreds of decision trees in parallel independently on different grid nodes.
• Randomly selects the input variables considered for splitting a node from all available inputs.
• Considers only a single variable that is most associated with the target for splitting.

High-performance count regression

• Fits regression models where the dependent variable represents counts (e.g., the number of events recorded in some period).
• Supports zero-inflated Poisson and negative binomial models.
• Estimates parameters by using the maximum likelihood method.

High-performance severity models

• Fits probability distributions for the severity (magnitude) of random events  (e.g., distributions of losses claimed under insurance policies as well as events with positive impacts such as the intermittent demand for certain products).
• Fits regression models for the scale of the severity distribution.
• Provides nine different probability distributions, including the Tweedie distribution.
• Allows users to add additional probability distributions.
• Can model data truncation and data censoring.