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- W2998884377 abstract "Predictive analytics has gained a lot of interest in present era. Statistical methods and machine learning techniques are applied on historical data to build a predictive model to forecast and recommend future events. Boosting is an ensemble machine learning technique which combines several low accuracy models to create a high accuracy model. It can be utilized in various domains for improving prediction such as credit, insurance, consumer behavior, medical diagnosis, and sales. This paper evaluates the use of AdaBoost and Gradient Boosting ensemble machine learning techniques for two-class prediction. A publicly available diabetes data set is used to assess the proposed model accuracy. The experimental result shows that prediction accuracy of Gradient Boosting algorithm is better than traditional machine learning and AdaBoost algorithm to classify patients with diabetes using diabetes risk factors." @default.
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- W2998884377 date "2019-12-20" @default.
- W2998884377 modified "2023-09-29" @default.
- W2998884377 title "Study of AdaBoost and Gradient Boosting Algorithms for Predictive Analytics" @default.
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- W2998884377 doi "https://doi.org/10.1007/978-981-15-0633-8_22" @default.
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