![]() Technically, we can define bias as the error between average model prediction and the ground truth. ) What is bias in machine learning?īias is a phenomenon that skews the result of an algorithm in favor or against an idea.īias is considered a systematic error that occurs in the machine learning model itself due to incorrect assumptions in the ML process. This article will examine bias and variance in machine learning, including how they can impact the trustworthiness of a machine learning model. Any issues in the algorithm or polluted data set can negatively impact the ML model. The user needs to be fully aware of their data and algorithms to trust the outputs and outcomes. Machine learning models cannot be a black box. This is further skewed by false assumptions, noise, and outliers. With larger data sets, various implementations, algorithms, and learning requirements, it has become even more complex to create and evaluate ML models since all those factors directly impact the overall accuracy and learning outcome of the model.
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