Abstract
This chapter introduces the fundamental to supervised machine learning algorithms, namely the classification and regression problems. We explain each technique using an inspiring example and discuss how the corresponding algorithms work together with the data engineering pipelines. They provide some guidelines for implementing a classification or regression task for other problems and required materials to evaluate the supervised learning being used.
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Notes
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There is another type of regression, named ordinal regression, where the dependent variables are of ordinal type, each showing a rank assigned to a sample within the dataset.
- 3.
In fact, it is the inverse of the covariance matrix if the data is normalized.
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Aldridge, I. (2013). High-frequency trading: A practical guide to algorithmic strategies and trading systems (Vol. 604). John Wiley & Sons.
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Mohammadi, M., Di Nucci, D. (2023). Supervised Machine Learning in a Nutshell. In: Liebregts, W., van den Heuvel, WJ., van den Born, A. (eds) Data Science for Entrepreneurship. Classroom Companion: Business. Springer, Cham. https://doi.org/10.1007/978-3-031-19554-9_6
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