CART 6.0 ProEX 資料挖掘分析軟體-資安軟體/研究分析軟體/心理學軟體/新永資訊有限公司

CART 6.0 ProEX 資料挖掘分析軟體

CART 6.0 ProEX 資料挖掘分析軟體

  • CART 6.0 ProEX 資料挖掘分析軟體
  • 編號
  • 類別
    研究分析軟體
  • 介紹
    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。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。
  • 價格

CART 6.0 ProEX

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)
 

CART 6.0 ProEX資料挖掘分析軟體

CARTSalford 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 ModelerCART®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。
 

CATPAC 文本分析軟體

CATPAC 是一個智能程序,可以閱讀任何文本並總結其主要想法。它不需要預編碼,也不需要語言假設。它在世界上許多世界上最重要的期刊上都有出色的研究記錄。到目前為止,這個世界上最大的數據是文本。與數字或分類尺度不同,文本允許人們以他們希望的任何方式表達自己。但是對文本的分析特別耗時並且受到讀者偏見的影響。Catpac克服了這兩個問題。

特價0

CATPAC 文本分析軟體
CATPAC 文本分析軟體

Miner3D 資料採擴分析軟體

資料分析不需很複雜。Miner3D 是容易的方法去創造想像力,極大量資料導向圖形。Miner3D 是一個結合視覺交互式環境做成基本資料分析和圖表。它的特色是強大功能的使用者界面以控制按鈕創造和自訂化您的圖表。財務專家、藥物研究員、生物科技或者材料研究、地質學家、採礦專家、加工和品質管理員、市場研究人員、銷售主任和許多其他專家使用Miner3D為做出快速地準確性的事業重大決定。

特價0

Miner3D 資料採擴分析軟體
Miner3D 資料採擴分析軟體

FastTEST Professional Testing System

FastTest 是一個全面且可配置的評估生態系統,旨在滿足各種測試組織的需求。FastTest建立在我們35年以上基於計算機的測試和項目庫存經驗的基礎上,涵蓋並推動整個評估生命週期,支持可靠性和有效性。

特價0

FastTEST Professional Testing System
FastTEST Professional Testing System

公司資訊

立即聯繫

透過以下方式迅速的聯絡我們

2018© Copyright All Rights Reserved

蘋果網頁設計
資安軟體量身規劃資訊安全零死角,提供有效方案,以期協助企業運用新科技改善及提升其商業服務及價值,資安軟體達到運用新科技最佳化商業營運及價值的目標。秉持讓客戶可以安心、輕鬆、有效的享用現代科技有線網路。資安軟體秉持讓客戶可以安心、輕鬆、有效的享用現代科技有線網路