Machine Learning Crash Course: The Bias-Variance Dilemma

Re:Machine Learning Crash Course: The Bias-Variance Dilemma by taeric on Tuesday July 18, 2017 @12:13AM (#14794222)

Sorry, I did not mean to be that they were the same. And, to that point, I was greatly projecting based on my beliefs that have grown. The number one thing I keep having to re-stress and learn is that these are probabilistic models.

So, if anything, I only meant they were related in that they are both good targets to internalize when working with ML. If there are better targets, I'm definitely interested in learning more.

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Re:Machine Learning Crash Course: The Bias-Variance Dilemma by xapata on Wednesday July 26, 2017 @03:35AM (#14854275)

Ultimately, everything is a probabilistic model. It'd be ridiculously impractical to try to incorporate all of one's knowledge into an analytic model. Instead, we generalize as appropriate and encapsulate the rest as a random variable, sometimes called "error". Plus, God plays dice and all that.

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