Sources of Error in Machine Learning

Author(s): Benjamin Obi Tayo Ph.D.

Originally published on Towards AI.

The solution to a machine learning problem is not unique. The predictive power of a model depends on the experience of the data scientist in dealing with sources of error

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Image by Benjamin O. Tayo

There is no such thing as a perfect machine learning model. A model’s overall reported error has incorporated into it contributions from the following sources:

Data collection can produce errors at different levels. For instance a survey could be designed for collecting data. However, individuals participating in the survey may not always provide the right information. For instance a participant may enter wrong information about their age, height, marital status, income, etc. Error in data collection could also occur when there is error in the system designed for recording and collecting the data, for instance a… Read the full blog for free on Medium.

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Published via Towards AI