TreeNet® software is Salford Systems’ most flexible and powerful data mining tool, responsible for at least a dozen prizes in major data mining competitions since its introduction in 2002

Features

Spline-based approximations to the TreeNet dependency plots

Exporting TreeNet dependency plots into XML file

Interactions: allow interactions penalty which inhibits TreeNet from introducing new variables (and thus interactions) within a branch of a tree

Auto creation of new spline-based approximation variables. One step creation and savings of transformed variable to new dataset

Automation: Build a series of models changing the minimum required size on child nodes (Automate MINCHILD)

Automation: Varying the number of "folds" used in cross-validation (Automate CVFOLDS)

Automation: Repeat cross-validation process many times to explore the variance of estimates (Automate CVREPEATED)

Automation: Build a series of models using a user-supplied list of binning variables for cross-validation (Automate CVBIN)

Automation: Check the validity of model performance using Monte Carlo shuffling of the target (Automate TARGETSHUFFLE)

Automation: Indicates whether a variable importance matrix report should be produced when possible (Automate VARIMP)

Automation: Saves the variable importance matrix to a comma-separated file (Automate VARIMPFILE)

Automation: Build a series of models by varying the random subsample size (Automate TNSUBSAMPLE)

Automation: Build a series of models by varying the quantile value when using the QUANTILE loss function(AutomateTNQUANTILE)

Automation: Build a series of models by varying the class weights between UNIT and BALANCED in N Steps (Automate TNCLASSWEIGHTS)

Auto creation of new spline-based approximation variables. One step creation and saving of transformed variable to new dataset

Flexible control over interactions in a TreeNet model (ICL)

Interaction strength reporting

Interactions: Generate reports describing pairwise interactions of predictors

Interaction Control Lists (ICL): gives you complete control over structural interactions allowed or not allowed during model building

Interactions: compute interaction statistics among predictors, for regression and logistic models only

Subsample separately by target class. Specify separate sampling rates for the target classes in binary logistic models

Control number of top ranked models for which performance measures will be computed and saved

Advanced controls to reduce required memory (RAM)

Extended Influence Trimming Controls: ability to limit influence trimming to focus class and/or correctly classified

Differential Lift Modeling (Netlift/Uplift)

Delta ROC Uplift as a performance measure

Uplift Profile tab for Uplift Results

TreeNet Newton Split Search and Regularization penalties (RGBoost) (TN NEWTON=YES, RGBL0, RGBL1, RGBL2)

Save information for further processing and individual tree predictions

TreeNet Monotonicity Controls

Added Sample with Replacement option to GUI dialog

Hessian to control tree growing in TreeNet

Newton-style splitting is now available for TreeNet Uplift loss

QUANTILE specifies which quantile will be used during LOSS=LAD

POISSON: Designed for the regression modeling of integer COUNT data

GAMMA distribution loss, used strictly for positive targets

NEGBIN: Negative Binomial distribution loss, used for counted targets (0,1,2,3,…)

COX where the target (MODEL) variable is the non-negative survival time while the CENSOR variable indicates

Tweedie loss function

New table showing "Top Interactions Pairs"

Control over number of bins reported in Uplift tables

Translation of models with INIT option

Random Selection of Predictors: first for tree then random subset from that list for node

Save detailed 2-way interaction statistics to a file

Control the depth of each tree

Automation: Build two linked models, where the first one predicts the binary event while the second one predicts the amount (Automate RELATED). For example, predicting whether someone will buy and how much they will spend

Automation: Build a series of models limiting the number of nodes in a tree (Automate NODES)

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) (Automate BIN)

Automation: Produces a series of three TreeNet models, making use of the TREATMENT variable specified on the TreeNet command (Automate DIFFLIFT)

Automation: Build a series of models varying the speed of learning (Automate LEARNRATE)

Automation: Build a series of models by progressively imposing additivity on individual predictors (Automate ADDITIVE)

Automation: Build a series of models utilizing different regression loss functions (Automate TNREG)

Automation: Build a series of models by varying subsampling fraction (Automate TNSUBSAMPLE)

Automation: Build a series of models using varying degree of penalty on added variables (Automate ADDEDVAR)

Automation: Explore the impact of influence trimming (outlier removal) for logistic and classification models (AUTOMATE INFLUENCE)

Automation: Stochastic search for the optimal regularization penalties (Automate TNRGBOOST)

Modeling Pipelines: RuleLearner, ISLE

Build a CART tree utilizing the TreeNet engine to gain speed as well as alternative reporting, and control over interactions using ICL

Build a RandomForests model utilizing the TreeNet engine to gain speed as well as partial dependency plots, spline approximatons, variable interaction statistics, and control over interactions using ICL

RandomForests inspired sampling of predictors at each node during model building

TreeNet Two-Variable dependency plots (3D plots) on-demand based on pairwise Interaction scores

TreeNet One-Variable dependency plots based on interaction scores

TreeNet in RandomForests mode for Classification

Random split selection (RSPLIT)

Median split selection (MSPLIT)

Automation: Explore the impact of influence trimming (outlier removal) for logistic and classification models (Automate INFLUENCE)

Automation: Exhaustive search and ranking for all interactions of the specified order (Automate ICL)

Automation: Varies the number of predictors that can participate in a TreeNet branch, using interaction controls to constrain interactions (Automate ICL NWAY)

Automation: Stochastic search of the core TreeNet modeling parameters (Automate TNOPTIMIZE)