Foresight provides one of the most accurate predictive engines available in the market today for classification and regression.
Unlike other algorithms, Foresight makes no assumptions about the relationships between a set of features in a feature space. Foresight can identify and determine the most relevant features used in a model and the model's feature dependencies. As a result, Foresight lends itself well to accurate non-linear modeling.
Equbits Foresight effectively deals with high dimensional feature spaces. The patent pending learning method avoids over-fitting and hence has the potential to deal with a large number of features, even in the low throughput case where there are few training examples.
Linear models make the assumption that features act indepedently of each other. Unfortunately, most models are highly non-linear. Using Equbits Foresight, users can automatically generate a non-linear classifiers and be confident that it will not over-fit. Equbits Foresight tuning methodology does not make any assumptions about correlations between features, as opposed to techniques that assume statistical independence.
Effectively Deals with Data Noise
Equbits Foresight provides packaged robust learners that are less impacted by noise as other classification and regression algorithms.
To learn more about Equbits Foresight, a data mining software application click here.
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"I tried several machine learning software, including in-house code, and found Equbits Foresight to be the only one to reach 100% true positive while keeping the number of false positives very low."
- Phillipe Luedi, Duke University
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Accuracy
Interpretability
