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An algorithm's bias and variance can be thought of as its property, this can be tweaked with things that we call as hyperparameters, but every algorithm has its own set of assumptions that it makes which if fulfilled, the algorithm performs better. Some algorithms such as Logistic Regression, linear-SVMs (not the kernel SVMs, because they can be used for non-...

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