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Towards a data science toolbox for industrial analytics applications

https://doi.org/10.1016/j.compind.2017.09.003
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Stochastic gradient boosting
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Analysis of a complex of statistical variables into principal components.
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Hierarchical Grouping to Optimize an Objective Function
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Bayesian optimization for predicting rare internal failures in manufacturing processes
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Information-theoretic metric learning
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To Explain or to Predict?
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Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)
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Predictive Analytics in Information Systems Research1
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Regression Towards Mediocrity in Hereditary Stature.
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Explaining Data-Driven Document Classifications1
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Comprehensible Predictive Models for Business Processes1
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GAN（Generative Adversarial Nets）
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Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
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Foundations of Machine Learning
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Algorithmic Accountability Reporting: On the Investigation of Black Boxes

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Incoming claims: where this work appears in other papers’ reference lists and arguments.

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