Comparative Study of Novel Machine Learning Algorithms on a Scalable Data Set
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Abstract
Scalability is a component of the concept or function which is capable of handling and performing well under an enhanced or large dataset. Scope of scalability in this concern can be related to data, features or interaction. The main problems are to basically resolve on the scalability issues related to supervised machine learning algorithms. The main aim of this research study is to evaluate and examine scalability of supervised machine learning algorithms. We have taken two novel supervised algorithms- L1 supervised algorithm and semi L1 supervised algorithm and applied on a massive data set of Brain tumor. Lastly the analysis based on various metrics viz. performance, accuracy, F1 score, recall and confusion matrix of the dataset with the two supervised machine learning algorithms are examined. The end results match with previous study even if on the medical domain massive data set, used in this research study.
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