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.
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Mohapatra, N., Shreya, K., Chinmay, A. (2020). Optimization of the Random Forest Algorithm. In: Borah, S., Emilia Balas, V., Polkowski, Z. (eds) Advances in Data Science and Management. Lecture Notes on Data Engineering and Communications Technologies, vol 37. Springer, Singapore. https://doi.org/10.1007/978-981-15-0978-0_19
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DOI: https://doi.org/10.1007/978-981-15-0978-0_19
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