Bias is a measure in Machine Learning of how prejudiced the outcome is. It is a phenomenon where the outcome of the algorithm is inaccurate due to the errors in the assumptions made while training the machine. Conscious or unconscious preferences may lead to built-in biases in algorithms. These can go unnoticed until the erroneous results have been put in use, which amplifies the error. Higher quality of a dataset leads to low bias, and one important aspect to look at is the randomness of the data. A true random dataset can cause low bias, while the data that lacks true randomness will lead to high-bias values in almost all of the cases.
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