新永資訊有限公司

CART是Salford Systems的旗艦數據挖掘軟件，該軟件是一款功能強大、易操作的決策樹，能自動篩選複雜的數據

終極分類樹：

Salford Predictive Modeler的CART®建模引擎是最終的分類樹，它徹底改變了高級分析領域，並開創了當前數據科學的時代。CART是現代數據挖掘中最重要的工具之一。

專有代碼：

從技術上講，CART建模引擎基於1984年由斯坦福大學和加州大學伯克利分校的四位世界知名統計學家引入的具有里程碑意義的數學理論。CART建模引擎是SPM的分類和回歸樹實現，是​​唯一體現原始專有代碼的決策樹軟件。

速度快，用途廣泛：

CART建模引擎的專利擴展專門用於增強市場研究和網絡分析的結果。CART建模引擎支持高速部署，允許Salford Predictive Modeler的模型大規模實時預測和評分。多年來，CART建模引擎已成為分析師可用的最流行且易於使用的預測建模算法之一，它也被用作許多基於裝袋和增強的現代數據挖掘方法的基礎。

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Classification and Regression Trees

CART® software is the ultimate classification tree that has revolutionized the field of advanced analytics, and inaugurated the current era of data science. CART is one of the most important tools in modern data mining.

Features

Linear Combination Splits

Optimal tree selection based on area under ROC curve

User defined splits for the root node and its children

Translating models into Topology

Edit and modify the CART trees via FORCE command structures

RATIO of the improvements of the primary splitter and the first competitor

Scoring of CV models as an Ensemble

Report impact of penalties in root node

New penalty against biased splits PENALTY BIAS (PENALTY / BIAS, CONTBIAS, CATBIAS)

Automation: Generate models with alternative handling of missing values (Automate MISSING_PENALTY)

Automation: Build a model using each splitting rule (six for classification, two for regression) (Automate RULES)

Automation: Build a series of models varying the depth of the tree (Automate DEPTH)

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

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

Automation: Explore accuracy versus speed trade-off due to potential sampling of records at each node in a tree (Automate SUBSAMPLE)

Automation: Generates a series of N unsupervised-learning models (Automate UNSUPERVISED)

Automation: Varies the RIN (Regression In the Node) parameter through the series of values (Automate RIN)

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: 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: 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: Generate models with alternative handling of missing values (AUTOMATE MVI)

Hotspot detection for Automate UNSUPERVISED

Hotspot detection for Automate TARGET

Hotspot detection to identify the richest nodes across the multiple trees

Differential Lift Modeling (Netlift/Uplift)

Profile tab in CART Summary window

Multiple user defined lists for linear combinations

Constrained trees

Ability to create and save dummy variables for every node in the tree during scoring

Report basic stats on any variable of user choice at every node in the tree

Comparison of learn vs. test performance at every node of every tree in the sequence

Automation: Vary the priors for the specified class (Automate PRIORS)

Automation: Build a series of models by progressively removing misclassified records thus increasing the robustness of trees and posssibly reducing model complexity (Automate REFINE)

Automation: Bagging and ARCing using the legacy code (COMBINE)

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

Automation: Build a series of models trying each available predictor as the root node splitter (Automate ROOT)

Automation: Explore the impact of favoring equal sized child nodes by varying CART’s end cut parameter (Automate POWER)

Automation: Explore the impact of penalty on categorical predictors (Automate PENALTY=HLC)

Build a Random Forests model utlizing the CART engine to gain alternative handling of missing values via surrogate splits (Automate BOOTSTRAP RSPLIT)