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- W4226316361 abstract "This chapter briefly presents a number of machine learning (ML) algorithms in a rather descriptive way. Support vector machine (SVM) is a supervised ML algorithm that can be used for both classification and regression challenges. Regression analysis deals with the problem of fitting straight lines to patterns of data. Logistic regression analysis studies the association between a categorical dependent variable and a set of independent (explanatory) variables. The decision tree is a type of supervised learning algorithm (having a predefined target variable) that is mostly used in classification problems. The chapter presents more details about the two most commonly used algorithms: Gradient Boosting and XGBoost. It provides more details on the performance analysis of the logistic regression. The chapter suggests a unified algorithmic framework for presenting these algorithms and describes the various splitting criteria and pruning methodologies." @default.
- W4226316361 created "2022-05-05" @default.
- W4226316361 date "2022-02-14" @default.
- W4226316361 modified "2023-09-27" @default.
- W4226316361 title "Machine Learning Algorithms" @default.
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- W4226316361 doi "https://doi.org/10.1002/9781119790327.ch2" @default.
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