Bias-Free Chemically Diverse Test Sets from Machine Learning

Learning from Point Sets with Observational Bias

Many objects can be represented as sets of multidimensional points. A common approach to learning from these point sets is to assume that each set is an i.i.d. sample from an unknown underlying distribution, and then estimate the similarities between these distributions. In realistic situations, however, the point sets are often subject to sampling biases due to variable or inconsistent observa...

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Machine Learning from Imbalanced Data Sets

For research to progress most effectively, we first should establish common ground regarding just what is the problem that imbalanced data sets present to machine learning systems. Why and when should imbalanced data sets be problematic? When is the problem simply an artifact of easily rectified design choices? I will try to pick the low-hanging fruit and share them with the rest of the worksho...

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Appendix : Machine Learning Bias Versus Statistical Bias

is if and 0 if. This high variance may help to explain why there is selection pressure for weak (machine learning) bias when the (machine learning) bias correctness is low. The reason that statisticians are interested in (statistical) bias and variance is that squared error is equal to the sum of squared (statistical) bias and variance. Therefore minimal (statistical) bias and minimal variance ...

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Appendix : Machine Learning Bias Versus Statistical Bias

is if and 0 if. This high variance may help to explain why there is selection pressure for weak (machine learning) bias when the (machine learning) bias correctness is low. The reason that statisticians are interested in (statistical) bias and variance is that squared error is equal to the sum of squared (statistical) bias and variance. Therefore minimal (statistical) bias and minimal variance ...

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Appendix : Machine Learning Bias Versus Statistical Bias

is if and 0 if. This high variance may help to explain why there is selection pressure for weak (machine learning) bias when the (machine learning) bias correctness is low. The reason that statisticians are interested in (statistical) bias and variance is that squared error is equal to the sum of squared (statistical) bias and variance. Therefore minimal (statistical) bias and minimal variance ...

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Bias-Free Chemically Diverse Test Sets from Machine Learning

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منابع مشابه

Learning from Point Sets with Observational Bias

Machine Learning from Imbalanced Data Sets

Appendix : Machine Learning Bias Versus Statistical Bias

Appendix : Machine Learning Bias Versus Statistical Bias

Appendix : Machine Learning Bias Versus Statistical Bias

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