ML-Flex by Stephen Piccolo.
From the webpage:
ML-Flex uses machine-learning algorithms to derive models from independent variables, with the purpose of predicting the values of a dependent (class) variable. For example, machine-learning algorithms have long been applied to the Iris data set, introduced by Sir Ronald Fisher in 1936, which contains four independent variables (sepal length, sepal width, petal length, petal width) and one dependent variable (species of Iris flowers = setosa, versicolor, or virginica). Deriving prediction models from the four independent variables, machine-learning algorithms can often differentiate between the species with near-perfect accuracy.
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Machine-learning algorithms have been developed in a wide variety of programming languages and offer many incompatible ways of interfacing to them. ML-Flex makes it possible to interface with any algorithm that provides a command-line interface. This flexibility enables users to perform machine-learning experiments with ML-Flex as a harness while applying algorithms that may have been developed in different programming languages or that may provide different interfaces.
ML-Flex is described at: jmlr.csail.mit.edu/papers/volume13/piccolo12a/piccolo12a.pdf
I don’t see any inconsistency in my interest in machine learning and thinking that users are the ultimate judges of semantics. Machine learning is a tool, much like indexes, concordances and other tools before it.
I first saw ML-Flex at KDnuggets.