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Getting Your Bias Variance Right and Regularization
2018
International Journal of Computer Applications
It is clear that with ever improving computational power and endless data, there have been more breakthroughs in Machine Learning. Some practices have clearly emerged as promising while building a neural network. A performance metric to judge the model, is to see if it is in the wrong side of bias or variance. While building a classifier, cases with high bias, and high variance crop up. This paper shall attempt to shed some light on the problem of bias-variance, and how to solve them, with some approaches to perform Regularization.
doi:10.5120/ijca2018917579
fatcat:ntp5lzfhjjhd5ld662hmcnzptq