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Splitting Criteria with the Bias Term

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Stream Data Mining: Algorithms and Their Probabilistic Properties

Part of the book series: Studies in Big Data ((SBD,volume 56))

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Abstract

The Mean Squared Error (MSE) of any estimator \(\widehat{\Theta }\) of some quantity \(\Theta \) is a sum of two terms

$$\begin{aligned} E\left[ \left( \widehat{\Theta }-\Theta \right) ^{2}\right] =E\left[ \left( \widehat{\Theta }-E\left[ \widehat{\Theta }\right] \right) ^{2}\right] + \left( E\left[ \widehat{\Theta }\right] -\Theta \right) ^{2}. \end{aligned}$$

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Authors and Affiliations

  1. Institute of Computational Intelligence, Czestochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski, Maciej Jaworski & Piotr Duda

  2. Information Technology Institute, University of Social Sciences, Lodz, Poland

    Leszek Rutkowski

Authors
  1. Leszek Rutkowski
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  2. Maciej Jaworski
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  3. Piotr Duda
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Correspondence to Leszek Rutkowski .

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Rutkowski, L., Jaworski, M., Duda, P. (2020). Splitting Criteria with the Bias Term. In: Stream Data Mining: Algorithms and Their Probabilistic Properties. Studies in Big Data, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-030-13962-9_6

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