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Salford Systems SPM

Name: Salford Systems SPM
Rating: 4.5 (2 reviews)

By Salford Systems

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Highlights : About Salford Systems SPM

Model Performance Stats based on Out of Bag Data During Bootstrapping

Correlation Computation of Over 10 Different Types of Correlation

Model Performance Stats based on Cross-Validation

Option to Save Processed Datasets into All Current Database/Statistical File Formats

Cross-Validation Models Can Be Scored as An Ensemble

Explore the Effects of the Learn Sample Size on the Model Performance

About Salford Systems SPM

Salford Systems SPM is a highly-accurate and ultra-fast suite of data mining tools and predictive modeling solutions designed to assist data scientists, analysts, modelers, and miners in dynamically and creatively creating and developing predictive, descriptive, and analytical models from databases and data sets, regardless of how large and complex such databases and data sets are and whatever organizations they come from. This award-winning and scalable platform is ideal for companies, organizations, and businesses who understand the value of data analysis, predictive analytics, and model generation to the performance of their activities and operations, letting them gain insights into their data and boost business growth and success. It is equipped with data mining technologies and modeling engines that support all aspect of data science projects which include classification, regression, survival analysis, missing value analysis, data binning, and clustering or segmentation. Furthermore, Salford Systems SPM comes with automation capabilties that enable users to ease the burden when it comes to exploring and refining their models. Although the platform provides them with full control over their data mining and model building processes, they have the option to set up the platform to automatically guess, suggest, and generate the best actions and steps that they can take. Also, Salford Systems SPM ensures that they are able to review and utilize alternative modeling techniques and approaches so they can weigh all possible results and outcomes.

Specifications

24/7 Support

Yes

Business Size

Mid-Market

Deployments

Cloud

Language Support

English

Platforms

Product Details

Features

Data Mining Tools and Modeling Engines

Nonlinear Regression

Bagging

Model Performance Stats based on Out of Bag Data During Bootstrapping

Translate Models into SaaS-Compatible Languages

Activity Window

Automatic Creation of Missing Value Indicators

Correlation Computation of Over 10 Different Types of Correlation

Automation Features

Explore Mutual Multivariate Dependencies

Automate Boostrapping Process

Decision Trees

Stochastic Gradient Boosting

Model Performance Stats based on Cross-Validation

Automatic Creation of Command Logs

Option to Save Processed Datasets into All Current Database/Statistical File Formats

Data Analysis Binning Engine

Advanced Imputation

Cross-Validation Models Can Be Scored as An Ensemble

Explore Model Stability

Explore the Effects of the Learn Sample Size on the Model Performance

Build a Series of Models

Benefits

Powerful Data Mining Technologies And Modeling Engines

One of the prominent features built within Salford System SPM is its utilization of various data mining technologies and modeling engines. With these technologies and engines, users will be able t accurately, efficiently, and effectively explore their data, build models, and apply different techniques in gaining meaningful and valuable insights into their data.

Generate Classification And Regression Trees

Among these data mining technologies and engines is CART. CART is in fact a software designed for making classification and regression trees. This software uses a fast and versatile predictive modeling algorithm that can expose underlying and hidden relationships in data which can’t be detected by other analytical tools.

Predictive Modeling That Can Anticipate Numeric Outcomes

Another technology and engine available in Salford Systems SPM is the software called MARS. Like CART, MARS can also produce classification and regression trees. However, this software can spot non-linear data relationships and interactions. In other words, users can use this software to discover trends and patterns in their data that are not easily detected using traditional classification and regression. Because of this, MARS is ideal for creating models for predicting outcomes that require numerical values. For example, MARS can anticipate the average monthly bill that a customer who is subscribed to mobile phone plan can pay or the amount of money that an online shopper can spend every time he or she visits a website or an eCommerce site.

Achieve A Higher Level Of Accuracy

Meanwhile, Salford Systems SPM has a data mining solution which is considered as the most powerful and flexible tool among all its data mining tools and modeling engines. This data mining tool is called TreeNet. TreeNet can achieve a level of precision and accuracy which is not typically attained using single models or ensembles like bagging and conventional boosting. It is actually immune to data errors and rejects data points that show rare, extremely dramatic, or impossible variations in relation to the parameters and requirements laid out by a particular model. It also has interaction detection capability which means it can check if a certain interaction is required for a given predictive analytics model. If users want to improve the accuracy and performance of their models, they must use this tool.

Bagging Tool

Lastly, the suite of data mining tools and predictive modeling solutions delivers a bagging tool called Random Forests. Bagging is a method applied in data science projects and optimization of algorithms which aims to reduce variance in models for specific data sets that such models are trained for but can still give room for bias. The concept of bagging can be compared to producing weaker copies of a strong brain and having those copies work on other data sets. Whatever results produced by each replica of the model are aggregated and the predictive power and capability of the model is then evaluated. Through bagging, users will be able to optimize the accuracy of a single model. Random Forests can quickly identify data anomalies and outliers, spot the most important predictors, show proximity clusters, substitute missing values with imputations, reveal patterns in data, precisely predict possible outcomes, and generate graphics that provide users with powerful insights.

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