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  • New automated features that accelerate modeling capabilities

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  • Latest algorithms that drive precision

  • New automated features that accelerate modeling capabilities

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SPM® Features

General Features

SPM® 8.2 General Features.

ComponentsBasicProProExUltra
ComponentsBasicProProExUltra
Modeling Engine: CART (Decision Trees) o o o o
Modeling Engine: MARS (Nonlinear Regression) o o o o
Modeling Engine: TreeNet (Stochastic Gradient Boosting) o o o o
Modeling Engine: RandomForests for Classification o o o o
Reporting ROC curves during model building and model scoring o o o o
Model performance stats based on Cross Validation o o o o
Model performance stats based on out of bag data during bootstrapping o o o o
Reporting performance summaries on learn and test data partitions o o o o
Reporting Gains and Lift Charts during model building and model scoring o o o o
Automatic creation of Command Logs o o o o
Built-in support to create, edit, and execute command files o o o o
Translating models into SAS-compatible language (all other languages supported for Pro and above) o o o o
Reading and writing datasets in all current database/statistical file formats, including csv file format o o o o
Option to save processed datasets into all current database/statistical file formats o o o o
Select Cases in Score Setup o o o o
TreeNet Scoring Offset in Score Setup o o o o
Setting of focus class supported for all categorical variables o o o o
Scalable limits on terminal nodes. This is a special mode that will ensure the ATOM and/or MINCHILD o o o o
Descriptive Statistics: Summary Stats, Stratified Stats, Charts and Histograms o o o
Activity Window: Brief data description, quick navigation to most common activities o o o
Additional Modeling Engines: Regularized Regression (LASSO/Ridge/LARS/Elastic Net/GPS) o o o
Data analysis Binning Engine o o o
Automatic creation of missing value indicators o o o
Option to treat missing value in a categorical predictor as a new level o o o
License to any level supported by RAM (currently 32MB to 1TB) o o o
License for multi-core capabilities o o o
Using built-in BASIC Programming Language during data preparation o o o
Automatic creation of lag variables based on user specifications during data preparation o o o
Automatic creation and reporting of key overall and stratified summary statistics for user supplied list of variables o o o
Display charts, histograms, and scatter plots for user selected variables o o o
Command Line GUI Assistant to simplify creating and editing command files o o o
Translating models into SAS/PMML/C/Java/Classic and ability to create classic and specialized reports for existing models o o o
Unsupervised Learning - Breiman's column scrambler o o o
Scoring any Automate (pre-packaged scenario of runs) as an ensemble model o o o
Summary statistics based on missing value imputation using scoring mechanism o o o
Impute options in Score Setup o o o
GUI support of SCORE PARTITIONS (GUI feature, SCORE PARTITIONS=YES) o o o
Quick Impute Analysis Engine: One-step statistical and model based imputation o o o
Advanced Imputation via Automate TARGET. Control over fill selection and new impute variable creation o o o
Correlation computation of over 10 different types of correlation o o o
Save OOB predictions from cross-validation models o o o
Custom selection of a new predictors list from an existing variable importance report o o o
User defined bins for Cross Validation o o o
Cross-Validation models can now be scored as an Ensemble o o o
An alternative to variable importance based on Leo Breiman's scrambler  o o o
Data Binning Results display (GUI feature) o o o
Data Binning Analysis Engine bins variables using model-based binning (via AUTOMATE BIN), or using weights of evidence coding. o o o
BIN ROUND, ADAPTIVEROUND methods (BIN METHOD=ROUND/ADAPTIVEROUND) o o o
Controls for number of Bins and Deciles (BOPTIONS NBINS, NDECILES) o o o
EVAL command and GUI display (GUI feature) o o o
Summary stats for the correlations (Correlation Stats tab) (GUI feature) o o o
TONUMERIC: create contiguous integer variables from other variables o o o
Automation: Build two models reversing the roles of the learn and test samples (Automate FLIP) o o o
Automation: Explore model stability by repeated random drawing of the learn sample from the original dataset (Automate DRAW) o o o
Automation: For time series applications, build models based on sliding time window using a large array of user options (Automate DATASHIFT) o o o
Automation: Explore mutual multivariate dependencies among available predictors (Automate TARGET) o o o
Automated imputation of all missing values (via Automate Target) o o o
Automation: Explore the effects of the learn sample size on the model performance (Automate LEARN CURVE) o o o
Automation: Build a series of models by varying the random number seed (Automate SEED) o o o
Automation: Explore the marginal contribution of each predictor to the existing model (Automate LOVO) o o o
Automation: Explore model stability by repeated repartitioning of the data into learn, test, and possibly hold-out samples (Automate PARTITION) o o o
Automation: Explore the nonlinear univariate relationships between the target and each available predictor (Automate ONEOFF) o o o
Automation: Bootstrapping process (sampling with replacement from the learn sample) with a large array of user options (Random Forests-style sampling of predictors, saving in-bag and out-of-bag scores, proximity matrix, and node dummies) (Automate BOOTSTRAP) *not available in RandomForests o o o
Automation: AUTOMATE ENABLETIMING=YES|NO to control timing reporting in Automates o o o
Save out of bag predictions during Cross Validation     o o
Use TREATMENT variables when scoring uplift models (SCORE EVAL)     o o
Use TREATMENT variables when evaluating uplift model predictions (EVAL)     o o
Automation: "Shifts" the "crossover point" between learn and test samples with each cycle of the Automate (Automate LTCROSSOVER)     o o
Automation: Build a series of models using different backward variable selection strategies (Automate SHAVING)     o o
Automation: Build a series of models using the forward-stepwise variable selection strategy (Automate STEPWISE)     o o
Automation: Explore nonlinear univariate relationships between each available predictor and the target (Automate XONY)     o o
Automation: Build a series of models using randomly sampled predictors (Automate KEEP)     o o
Automation: Explore the impact of a potential replacement of a given predictor by another one (Automate SWAP)     o o
Automation: Parametric bootstrap process (Automate PBOOT)     o o
Automation: Build a series of models for each strata defined in the dataset (Automate STRATA)     o o
Automation: Generate detailed univariate stats on every continuous predictor to spot potential outliers and problematic records (AUTOMATE OUTLIERS)     o o
Automation: Convert (bin) all continuous variables into categorical (discrete) versions using a large array of user options (equal width, weights of evidence, Naïve Bayes, superwised) (AUTOMATE BIN)     o o
Automation: Build a series of models using every available data mining engine (Automate MODELS)     o
Automation: Run TreeNet for Predictor selection, Auto-bin predictors, then build a series of models using every available data mining engine (Automate GLM)     o
Modeling Pipelines: RuleLearner, ISLE     o

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Tags: Features, v8, Salford Predictive Modeler, SPM, Salford-Systems

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