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Additional TreeNet Features

Additional TreeNet Features

ComponentsBasicProProExUltra
ComponentsBasicProProExUltra
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
Interactions: allow interactions penalty which inhibits TreeNet from introducing new variables (and thus interactions) within a branch of a tree   o o o
Automation: Build a series of models changing the minimum required size on child nodes (Automate MINCHILD)   o o o
Automation: Varying the number of "folds" used in cross-validation (Automate CVFOLDS)   o o o
Automation: Repeat cross-validation process many times to explore the variance of estimates (Automate CVREPEATED)   o o o
Automation: Build a series of models using a user-supplied list of binning variables for cross-validation (Automate CVBIN)   o o o
Automation: Check the validity of model performance using Monte Carlo shuffling of the target (Automate TARGETSHUFFLE)   o o o
Automation: Indicates whether a variable importance matrix report should be produced when possible (Automate VARIMP)   o o o
Automation: Saves the variable importance matrix to a comma-separated file (Automate VARIMPFILE)   o o o
Auto creation of new spline-based approximation variables. One step creation and savings of transformed variable to new dataset     o o
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   o o
Flexible control over interactions in a TreeNet model(ICL)     o o
Interaction strength reporting     o o
Interactions: Generate reports describing pairwise interactons of predictors     o o
Subsample separately by target class. Specify separate sampling rates for the target classes in binary logistic models     o o
Control number of top ranked models for which performance measures will be computed and saved     o o
Advanced controls to reduce required memory (RAM)     o o
Extended Influence Trimming Controls: ability to limit influence trimming to focus class and/or correctly classified     o o
Differential Lift Modeling (Netlift/Uplift)     o o
QUANTILE specifies which quantile will be used during LOSS=LAD     o o
POISSON: Designed for the regression modeling of integer COUNT data     o o
GAMMA distribution loss, used strictly for positive targets     o o
NEGBIN: Negative Binomial distribution loss, used for counted targets (0,1,2,3,…)     o o
COX where the target (MODEL) variable is the non-negative survival time while the CENSOR variable indicates     o o
Tweedie loss function     o o
Automation: Build a series of models limiting the number of nodes in a tree (Automate NODES)     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) (Automate BIN)     o o
Automation: Produces a series of three TreeNet models, making use of the TREATMENT variable specified on the TreeNet command (Automate DIFFLIFT)     o o
Automation: Build a series of mdoels varying the speed of learning (Automate LEARNRATE)     o o
Automation: Build a series of models by progressively imposing additivity on individual predictors (Automate ADDITIVE)     o o
Automation: Build a series of models utilizing different regression loss functions (Automate TNREG)     o o
Automation: Build a series of models by varying subsampling fraction (Automate TNSUBSAMPLE)     o o
Automation: Build a series of models using varying degree of penalty on added variables (Automate 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
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 (Automate INFLUENCE)       o
Automation: Exhaustive search and ranking for all interactions of the specified order (Automate ICL)       o
Automation: Varies the number of predictors that can participate in a TreeNet branch, using interaction controls to constrain interactions (Automate ICL NWAY)       o

 

Additional TreeNet  Features

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Tags: TreeNet, Salford Predictive Modeler, SPM, Salford-Systems

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