Titlebook: ;

abduction

手勢

諄諄教誨

胰臟

返老還童

Social Systems and Learning Systemsations. We intend to apply various modeling techniques to extract models from the data. Although we have not yet discussed any modeling technique in greater detail (see the following chapters), we have already glimpsed at some fundamental techniques and potential pitfalls in the previous chapter. Be

細胞

Richard S. Ostfeld,Lorrie L. Klosterman the identification of areas that exceptionally deviate from the remainder. They provide answers to questions such as: Does it naturally subdivide into groups? How do attributes depend on each other? Are there certain conditions leading to exceptions from the average behavior? The chapter provides a

tangle

Reflection, Theory and Language,rder to group similar objects. In this chapter we will discuss methods that address a very different setup: Instead of finding structure in a data set, we are now focusing on methods that find explanations for an unknown dependency within the data. Such a search for a dependency usually focuses on a

ingestion

https://doi.org/10.1007/978-3-030-78324-2e discussed methods for basically the same purpose, the methods in this chapter yield models that do not help much to explain the data or even dispense with models altogether. Nevertheless, they can be useful, namely if the main goal is good prediction accuracy rather than an intuitive and interpret

prick-test

Testing the Explanatory Value of Naturereted to gain new insights for feature construction (or even data acquisition). What we have ignored so far is the deployment of the models into production as well as their continued monitoring and potentially even automatic updating.

正論

Guide to Intelligent Data Science978-3-030-45574-3Series ISSN 1868-0941 Series E-ISSN 1868-095X