Analytic Solver Data Mining provides a facility to compute various metrics to give users information on what features should be included, or excluded, from their models.

In many cases the most accurate models, or the models with the lowest misclassification or residual errors, have benefited from better feature selection, using a combination of human insights and automated methods. Feature Selection attempts to discover a subset of the original variables, while Feature Extraction attempts to map a high-dimensional model to a lower-dimensional space.Īnalytic Solver Data Mining’s Feature Selection tool gives users the ability to rank and select the most relevant variables for inclusion in a classification or prediction model. This domain can be divided into two branches, feature selection and feature extraction. The Explore tab provides access to Dimensionality Reduction via Feature Selection, and the ability to explore data using charts such as Bar Charts, Line Charts, Scatterplots, Boxplots, Histograms, Parallel Coordinates, ScatterPlot Matrices and Variable Plots.ĭimensionality Reduction is the process of deriving a lower-dimensional representation of original data, which still captures the most significant relationships to be used to represent the original data in a model.

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