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- W2999324871 abstract "Optimization algorithms are implemented for making the field of machine learning more efficient by comparing various solutions until an optimum or a satisfactory answer is found to yield a better accuracy score than the earlier existing one. In this paper, optimization of the Random Forest is performed which is a supervised learning model for classification and regression. A detailed analysis of the optimization technique of this model is done, which follows the unequal weight voting strategy, where weight is assigned based on how well an individual tree performs." @default.
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- W2999324871 date "2020-01-01" @default.
- W2999324871 modified "2023-10-16" @default.
- W2999324871 title "Optimization of the Random Forest Algorithm" @default.
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- W2999324871 doi "https://doi.org/10.1007/978-981-15-0978-0_19" @default.
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