Titlebook: Machine Learning for Practical Decision Making; A Multidisciplinary Christo El Morr,Manar Jammal,Walid EI-Hallak Textbook 2022 The Editor(

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书目名称Machine Learning for Practical Decision Making影响因子(影响力)学科排名




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Boosting and Stacking,The ensemble technique relies on an aggregate of models’ output to provide a better prediction. Other than voting and bagging, we can use boosting and stacking.

FAST

Decision Trees, label the instance as belonging to a class. The decision tree is our first approach to solve classification problems. However, decision trees can perform regression too, hence their name classification and regression trees (CART). The random forests that we will encounter in a later chapter are powerful variations of CART.

联想记忆

,Naïve Bayes,uld be a yes or no with certainty. The situation with Bayesian modeling for decision-making is different—it estimates the probability that an instance belongs to a certain class, which is more nuanced [1].

泄露

Neural Networks,simply neural networks, are effective in solving complex problems, i.e., in modeling complex nonlinear functions. ANN. model the functioning of the brain’s neurons; ANN can be trained to “learn” how to recognize patterns and classify data [1].

chuckle

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