SPM v7.0 Feature Matrix

Salford Systems
General Features SPM 7.0 Feature Matrix
Components Basic Pro ProEx Ultra
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 o o o o
Reading and writing datasets in all current database/statistical
file formats
o o o o
Option to save processed datasets into all current
database/statistical file formats
o o o o
Automation: Build a series of models using every available data
mining engine (Battery MODELS)
o o o o
9685 Via Excelencia, Suite 208, San Diego, CA, 92126
Tel: 619-543-8880 Fax: 619-543-8888 E-Mail: info@salford-systems.com Web Site: www.salford-systems.com

Salford Systems
Additional Modeling Engines: Regression, Logistic
Regression, RandomForests for Regession, Regularized
Regression (LASSO/Ridge/LARS/Elastic Net/GPS)
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 o o o
An alternative to variable importance based on Leo Breiman's
scrambler
o o o
Unsupervised Learning - Breiman's column scrambler o o o
Scoring any Battery (pre-packaged scenario of runs) as an
ensemble model
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
Automated imputation of all missing values o o o
Automation: Build two models reversing the roles of the learn and test
samples (Battery FLIP)
o o o
Automation: Explore model stability by repeated random drawing of the
learn sample from the original dataset (Battery DRAW)
o o o
Automation: For time series applications, build models based on
sliding time window using a large array of user options (Battery
DATASHIFT)
o o o
Automation: Explore mutual multivariate dependencies among
available predictors (Battery TARGET)
o o o
Automation: Explore the effects of the learn sample size on the model
performance (Battery SAMPLE)
o o o

Salford Systems
Automation: Explore alternative strategies to handling of missing
values (Battery MVI)
o o o
Automation: Check the validity of model performance using Monte
Carlo shuffling of the target (Battery MCT)
o o o
Automation: Build a series of models varying the number of bins for
Cross Validation (Battery CV)
o o o
Automation: Repeat Cross Validation process many times to explore
the variance of estimates (Battery CVR)
o o o
Automation: Build a series of models using a user-suppllied list of
binning variables for cross-validation (Battery CVBIN)
o o o
Automation: Build a series of models by varying the random number
seed (Battery SEED)
o o o
Automation: Explore the marginal contribution of each predictor to the
existing model (Battery LOVO)
o o o
Save out of bag predictions during Cross Validation o o
Automation: Generate detailed univariate stats on every
continuous predictor to spot potential outliers and problematic
records (Battery 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, supervised)
(Battery BIN)
o o
Automation: Explore model stability by repeated repartitioning of the
data into learn, test, and possibly hold-out samples (Battery
PARTITION)
o o
Automation: Build a series of models using different backward variable
selection strategies (Battery SHAVING)
o o
Automation: Build a series of models using the forward-stepwise
variable selection strategy (Battery STEPWISE)
o o
Automation: Explore nonlinear univariate relationships between the
target and each available predictor (Battery ONEOFF, Battery XONY)
o o
Automation: Build a series of models using randomly sampled
predictors (Battery KEEP)
o o
Automation: Explore the impact of a potential replacement of a given
predictor by another one (Battery SWAP)
o o

Salford Systems
Automation: Explore the impact of penalty on categorical predictors
(Battery PENALTY=HLC)
o o
Automation: Explore the impact of penalty on missing values (Battery
PENALTY=MISSING)
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) (Battery BOOTSTRAP)
o o
Automation: Parametric bootstrap process (Battery PBOOT) o o
Automation: Build a series of models for each strata defined in the
dataset (Battery STRATA)
o o
Automation: Build two linked models, where the first one predicts the
binary event while the second one predicts the amount (Battery
RELATED). For example, predicting whether someone will buy and how
much will be spent
o o
Automation: Build a series of models limiting the number of nodes in a
tree thus controlling the order of interactions (Battery NODES)
o o
Automation: Build a series of models varying the speed of learning
(Battery LEARNRATE)
o o
Automation: Build a series of models by progressively imposing
additivity on individual predictors (Battery ADDITIVE)
o o
Automation: Build a series of models utilizing different regression loss
functions (Battery TNREG)
o o
Automation: Build a series of models by varying subsampling fraction
(Battery TNSUBSAMPLE)
o o
Automation: Build a series of models using varying degree of penalty
on added variables (Battery ADDEDVAR)
o o
Modeling Pipelines: RuleLearner, ISLE o
Build a CART tree utilizing the TreeNet engine to gain speed as
well as alternative reporting
o
RandomForests inspired sampling of predictors at each node
during model building
o
Build a RandomForests model utilizing the TreeNet engine to
gain speed as well as alternative reporting
o
Build a Random Forests model utilizing the CART engine to gain
alternative handling of missing values via surrogate splits
(Battery BOOTSTRAP RSPLIT)
o

Salford
Systems

9685
Via
Excelencia,
Suite
208,
San
Diego,
CA,
92126
Tel:
619-‐543-‐8880
Fax:
619-‐543-‐8888
E-‐Mail:
info@salford-‐systems.com
Web
Site:
www.salford-‐systems.com
Additional
CART
Features SPM
7.0
Feature
Matrix
Modeling Engine: CART (Decision Trees) o o o o
Linear Combination Splits o o o o
Optimal tree selection based on area under ROC curve o o o o
User defined splits for the root node and its children o o o
Automation: Generate models with alternative handling of
missing values (Battery MVI)
o o o
Automation: Build a series of models using all available splitting
strategies (six for classification, two for regression) (Battery RULES)
o o o
Automation: Build a series of models varying the depth of the tree
(Battery DEPTH)
o o o
Automation: Build a series of models changing the minimum
required size on parent nodes (Battery ATOM)
o o o
required size on child nodes (Battery MINCHILD)
o o o
Automation: Explore accuracy versus speed trade-off due to
potential sampling of records at each node in a tree (Battery
SUBSAMPLE)
o o o
Multiple user defined lists for linear combinations o o
Constrained trees o o
Ability to create and save dummy variables for every node in
the tree during scoring
o o
Report basic stats on any variable of user choice at every
node in the tree
o o
Comparison of learn vs. test performance at every node of
every tree in the sequence
o o
Hot-Spot detection to identify the richest nodes across multiple
trees
o o
Automation: Vary the priors for the specified class (Battery
PRIORS)
o o
Automation: Build a series of models limiting the number of nodes in
a tree (Battery NODES)
o o
Automation: Build a series of models trying each available predictor
as the root node splitter (Battery ROOT)
o o
Automation: Explore the impact of favoring equal sized child nodes
(Battery POWER)
o o

Salford
Systems

9685
Via
Excelencia,
Suite
208,
San
Diego,
CA,
92126
Tel:
619-‐543-‐8880
Fax:
619-‐543-‐8888
E-‐Mail:
Web
Site:
Automation: Build a series of models by progressively removing
misclassified records thus increasing the robustness of trees and
posssibly reducing model complexity (Battery REFINE)
o o
Automation: Bagging and ARCing using the legacy code
(COMBINE)
o o
Build a CART tree utilizing the TreeNet engine to gain speed
as well as alternative reporting
o
Build a Random Forests model utlizing the CART engine to
gain alternative handling of missing values via surrogate splits
(Battery BOOTSTRAP RSPLIT)
o

Salford
Systems

9685
Via
Excelencia,
Suite
208,
San
Diego,
CA,
92126
Tel:
619-‐543-‐8880
Fax:
619-‐543-‐8888
E-‐Mail:
Web
Site:
Additional
MARS
Features SPM
7.0
Feature
Matrix
Modeling Engine: MARS (Nonlinear Regression) o o o o
Automation: Build a series of models varying the maximum
number of basis functions (Battery BASIS)
o o o
Automation: Build a series of models varying the smoothness
parameter (Battery MINSPAN)
o o
Automation: Build a series of models varying the order of
interactions (Battery INTERACTIONS)
o o
Automation: Build a series of models varying the modeling speed
(Battery SPEED)
o o
Automation: Build a series of models using varying degree of
penalty on added variables (Battery PENALTY MARS)
o

Salford
Systems

9685
Via
Excelencia,
Suite
208,
San
Diego,
CA,
92126
Tel:
619-‐543-‐8880
Fax:
619-‐543-‐8888
E-‐Mail:
Web
Site:
Additional
TreeNet
Features SPM
7.0
Feature
Matrix
Modeling Engine: TreeNet (Stochastic Gradient Boosting) o o o o
Spline-based approximations to the TreeNet dependency plots o o o
Exporting TreeNet dependency plots into XML file o o o
required size on child nodes (Battery MINCHILD)
o o o
Flexible control over interactions in a TreeNet model (ICL) o o
Interaction strength reporting o o
Build a CART tree utilizing the TreeNet engine to gain speed
as well as alternative reporting
o
Build a RandomForests model utilizing the TreeNet engine to
gain speed as well as alternative reporting
o
RandomForests inspired sampling of predictors at each node
during model building
o
Automation: Explore the impact of influence trimming (outlier
removal) for logistic and classification models (Battery INFLUENCE)
o
Automation: Exhaustive search and ranking for all interactions of
the specified order (Battery ICL)
o

Salford
Systems

9685
Via
Excelencia,
Suite
208,
San
Diego,
CA,
92126
Tel:
619-‐543-‐8880
Fax:
619-‐543-‐8888
E-‐Mail:
Web
Site:
Additional
Random
Forests
Features SPM
7.0
Feature
Matrix
Modeling Engine: RandomForests for Classification o o o o
Additional Modeling Engine: Random Forests for Regession o o o
Automation: Vary the number of randomly selected predictors
at the node-level (Battery RFNPREDS)
o o o

Salford Systems
Additional GPS Features SPM 7.0 Feature Matrix
Modeling Engines: Regularized Regression
(LASSO/Ridge/LARS/Elastic Net/GPS)
o o o
Automation: Build a series of models by forcing different limit on the
maximum correlation among predictors (Battery MAXCORR)
o o

SPM v7.0 Feature Matrix

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