计量预测软件

MARS是一个先进的计量预测软件,它包括数据库、自建模系统、优化系统,适用于材料研究数据和生产数据的管理,多元非线性建模,以及多因子、多目标优化。MARS非常适合希望以类似传统回归的形式获得结果，同时又能捕获基本的非线性和相互作用的用户。

自动非线性回归

MARS

高质量的回归和分类

MARS模型旨在预测数字结果，例如移动电话客户的平均每月账单或购物者预期在 站访问中花费的金额。MARS引擎还能够为是/否结果生成高质量的分类模型。MARS引擎自动且高速地执行变量选择，变量变换，交互检测和自检。

高绩效结果

MARS表现出非常高性能的领域包括预测发电公司的电力需求，将客户满意度评分与产品的工程规范相关联，以及地理信息系统（GIS）中的存在/不存在建模。

MARS功能：

MARS可在预测范围内建立多个线性回归模型。它通过对数据进行分区来执行此操作，然后在每个不同的分区上运行线性回归模型。

MARS算法是线性模型的扩展，它不对响应变量和预测之间的关系做出假设。虽然广义线性模型和广义加法模型假定预测变量的系数在预测变量的所有值上都是恒定的，但MARS算法专门考虑到了这种情况通常并非如此。但是，MARS算法还与机器学习模型（例如基于树的模型）相似，因为它使用了类似的迭代方法。

MARS software is ideal for users who prefer results in a form similar to traditional regression while capturing essential nonlinearities and interactions. The MARS approach to regression modeling effectively uncovers important data patterns and relationships that are difficult, if not impossible, for other regression methods to reveal. MARS builds its model by piecing together a series of straight lines with each allowed its own slope. This permits MARS to trace out any pattern detected in the data.

HIGH-QUALITY PROBABILITY

The MARS model is designed to predict continuous numeric outcomes such as the average monthly bill of a mobile phone customer or the amount that a shopper is expected to spend in a web site visit. MARS is also capable of producing high quality probability models for a yes/no outcome. MARS performs variable selection, variable transformation, interaction detection, and self-testing, all automatically and at high speed.

HIGH-PERFORMANCE RESULTS

Areas where MARS has exhibited very high-performance results include forecasting electricity demand for power generating companies, relating customer satisfaction scores to the engineering specifications of products, and presence/absence modeling in geographical information systems (GIS).

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