DQS Academy (HK) provides Salford Predictive Modeler application training and licensed SPM software, a powerful big data mining, analysis and modeling tool widely used for investment, insurance, health, marketing, product and service improvement.
DQS學堂 (HK) 提供 SPM 應用培訓 和 SPM 正版軟件,一個功能強大的廣泛用於金融、保險、健康、市場營銷、產品和服務改善的大數據挖掘、分析和建模工具

 “In God we trust, all others bring data.”   — Dr. Deming
 “上帝,我們信任,其他的看數據。”    — 戴明 博士 

Introduction

The Salford Predictive Modeler® (SPM) software suite is a highly accurate and ultra-fast machine learning tool for developing predictive, descriptive, and analytical models from databases and data sets of any size, complexity, or organization. SPM software is a product of Salford Systems, a subsidiary of Minitab.

The SPM software’s data mining technologies span classification, regression, survival analysis, missing value analysis, data binning and clustering/segmentation to cover all aspects of your data science projects.

The SPM software suite’s automation accelerates the process of model building by conducting substantial portions of the model exploration and refinement process for the analyst. While the analyst is always in full control we optionally anticipate the analysts’ next best steps and package a complete set of results from alternative modeling strategies for easy review.

SPM 可從任何規模、複雜或組織的資料庫和數據集,用於開發可預測、描述和分析模型,是一個高度準確且超效率的機器學習工具。相對於傳統的統計分析方法,這種數據挖掘建模分析方法在數據齊整性上要求不高,並可以應用於很多自變量參數和數量巨大的數據。

 

Salford Predictive Modeler (SPM) 大數據分析軟件
Item No.
產品代碼
License Type
許可類別
Duration
使用期限
Delivery Location
交貨地點
Price
價格
SPM-S1Single-user License
單機版
A year 1年by electronic way from HK
從香港經電子途徑
Request a quote 要求報價
SPM-U1Single-user Upgrade
單機版升級
as aboveas aboveRequest a quote 要求報價
SPM-MSMulti-user License
多用戶授權
(Server version 服務器版)
as aboveas aboveRequest a quote 要求報價
Feature List 功能清單

Components Basic Pro ProEx Ultra
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 (all other languages supported for Pro and above) o o o o
Reading and writing datasets in all current database/statistical file formats, including csv file format o o o o
Option to save processed datasets into all current database/statistical file formats o o o o
Select Cases in Score Setup o o o o
TreeNet Scoring Offset in Score Setup o o o o
Setting of focus class supported for all categorical variables o o o o
Scalable limits on terminal nodes. This is a special mode that will ensure the ATOM and/or MINCHILD o o o o
Descriptive Statistics: Summary Stats, Stratified Stats, Charts and Histograms o o o
Activity Window: Brief data description, quick navigation to most common activities o o o
Additional Modeling Engines: Regularized Regression (LASSO/Ridge/LARS/Elastic Net/GPS) o o o
Data analysis Binning Engine 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 and ability to create classic and specialized reports for existing models o o o
Unsupervised Learning – Breiman’s column scrambler o o o
Scoring any Automate (pre-packaged scenario of runs) as an ensemble model o o o
Summary statistics based on missing value imputation using scoring mechanism o o o
Impute options in Score Setup o o o
GUI support of SCORE PARTITIONS (GUI feature, SCORE PARTITIONS=YES) o o o
Quick Impute Analysis Engine: One-step statistical and model based imputation o o o
Advanced Imputation via Automate TARGET. Control over fill selection and new impute variable creation o o o
Correlation computation of over 10 different types of correlation o o o
Save OOB predictions from cross-validation models 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
Cross-Validation models can now be scored as an Ensemble o o o
An alternative to variable importance based on Leo Breiman’s scrambler o o o
Data Binning Results display (GUI feature) o o o
Data Binning Analysis Engine bins variables using model-based binning (via AUTOMATE BIN), or using weights of evidence coding. o o o
BIN ROUND, ADAPTIVEROUND methods (BIN METHOD=ROUND/ADAPTIVEROUND) o o o
Controls for number of Bins and Deciles (BOPTIONS NBINS, NDECILES) o o o
EVAL command and GUI display (GUI feature) o o o
Summary stats for the correlations (Correlation Stats tab) (GUI feature) o o o
TONUMERIC: create contiguous integer variables from other variables o o o
Automation: Build two models reversing the roles of the learn and test samples (Automate FLIP) o o o
Automation: Explore model stability by repeated random drawing of the learn sample from the original dataset (Automate DRAW) o o o
Automation: For time series applications, build models based on sliding time window using a large array of user options (Automate DATASHIFT) o o o
Automation: Explore mutual multivariate dependencies among available predictors (Automate TARGET) o o o
Automated imputation of all missing values (via Automate Target) o o o
Automation: Explore the effects of the learn sample size on the model performance (Automate LEARN CURVE) o o o
Automation: Build a series of models by varying the random number seed (Automate SEED) o o o
Automation: Explore the marginal contribution of each predictor to the existing model (Automate LOVO) o o o
Automation: Explore model stability by repeated repartitioning of the data into learn, test, and possibly hold-out samples (Automate PARTITION) o o o
Automation: Explore the nonlinear univariate relationships between the target and each available predictor (Automate ONEOFF) o 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) (Automate BOOTSTRAP) *not available in RandomForests o o o
Automation: AUTOMATE ENABLETIMING=YES|NO to control timing reporting in Automates o o o
Save out of bag predictions during Cross Validation o o
Use TREATMENT variables when scoring uplift models (SCORE EVAL) o o
Use TREATMENT variables when evaluating uplift model predictions (EVAL) o o
Automation: “Shifts” the “crossover point” between learn and test samples with each cycle of the Automate (Automate LTCROSSOVER) o o
Automation: Build a series of models using different backward variable selection strategies (Automate SHAVING) o o
Automation: Build a series of models using the forward-stepwise variable selection strategy (Automate STEPWISE) o o
Automation: Explore nonlinear univariate relationships between each available predictor and the target (Automate XONY) o o
Automation: Build a series of models using randomly sampled predictors (Automate KEEP) o o
Automation: Explore the impact of a potential replacement of a given predictor by another one (Automate SWAP) o o
Automation: Parametric bootstrap process (Automate PBOOT) o o
Automation: Build a series of models for each strata defined in the dataset (Automate STRATA) o o
Automation: Generate detailed univariate stats on every continuous predictor to spot potential outliers and problematic records (AUTOMATE 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, superwised) (AUTOMATE BIN) o o
Automation: Build a series of models using every available data mining engine (Automate MODELS) o
Automation: Run TreeNet for Predictor selection, Auto-bin predictors, then build a series of models using every available data mining engine (Automate GLM) o
Modeling Pipelines: RuleLearner, ISLE

System Requirements 操作系統要求

  • Suggested minimum and recommended system requirements:

    • 80486 processor or higher.
    • 512MB of random-access memory (RAM). This value depends on the “size” you have purchased (64MB, 128MB, 256MB, 512MB, 1GIG). While all versions may run with a minimum of 32MB of RAM, we CANNOT GUARANTEE they will. We highly recommend that you follow the recommended memory configuration that applies to the particular version you have purchased. Using less than the recommended memory configuration results in hard drive paging, reducing performance significantly, or application instability.
    • Hard disk with 40 MB of free space for program files, data file access utility, and sample data files.
    • Additional hard disk space for scratch files (with the required space contingent on the size of the input data set).
    • CD-ROM or DVD drive.

    Recommended System Requirements

    Because Salford Tools are extremely CPU intensive, the faster your CPU, the faster they will run. For optimal performance, we strongly recommend they run on a machine with a system configuration equal to, or greater than, the following:

    • Pentium 4 processor running 2.0+ GHz.
    • 2 GIG of random-access memory (RAM). This value depends on the “size” you have purchased (64MB, 128MB, 256MB, 512MB, 1GIG). While all versions may run with a minimum of 32MB of RAM, we CANNOT GUARANTEE they will. We highly recommend that you follow the recommended memory configuration that applies to the particular version you have purchased. Using less than the recommended memory configuration results in hard drive paging, reducing performance significantly, or application instability.
    • Hard disk with 40 MB of free space for program files, data file access utility, and sample data files.
    • Additional hard disk space for scratch files (with the required space contingent on the size of the input data set).
    • CD-ROM or DVD drive.
    • 2 GIG of additional hard disk space available for virtual memory and temporary files.

    Ensuring Proper Permissions

    If you are installing on a machine that uses security permissions, please read the following note.

    • You must belong to the Administrator group on Windows 2008, Windows 7 / 8 to be able to properly install and license. Once the application is installed and licensed, any member with read/write/modify permissions to the applications /bin and temp directories can execute and run the application.

Licensing Application

The Salford Predictive Modeler uses a system of application system ID and associated unlock key. When installation is complete, the user will need to email the application “system ID.” This system ID is clearly displayed in the License Information displayed the first time the application is started. You can alternatively get to this window by selecting the Help->License menu option.

Method 1: Fixed License
With a fixed license, each machine must have its own copy of the licensed program installed. If your license terms permit more than one copy, then the license must be activated on each machine that will be used.

Method 2: Floating License
This method of licensing your program is used if you intend the program application to be used by more than one user concurrently over a network. A floating license tracks the number of copies “checked out.” When that number exceeds your license terms, a message is provided informing the user “all copies are checked out.” The licensed program may be installed on a machine that each client machine can access. Machines that are not connected to the network must be issued a fixed license (Method 1 above).

A floating license is particularly useful when the number of potential users exceeds the number of seats specified in your license terms.

Terms 條款
a) The listed price for SPM 8.2 software includes:
– a link for software download,
– license code(s),
– Salford Systems’ official guides for installation,

b) The installation and registration will be done by the users.

c) License Agreement for SPM software is as published by Salford Systems.

d) The users can buy training courses on the use of SPM, with terms listed here.

On-line Order 在線下單 Enquiry or Free Trial  查詢 或 免費試用