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Ways to improve the quality of Machine Learning
- Choose the right data set: one that pertains and sticks to your needs and does not wander off from that course in high magnitudes. Say, for example, your model needs images of human faces, but rather your data set is more of an assorted set of various body parts. It will only lead to poor results in the end.
- Make sure that your device/workstation is devoid of any pre-existing bias which would be impossible for any kind of math/statistics to catch. Say, for example, a system contains a scale that has been trained to round-off a number to its nearest hundred. In the event your model contains precise calculations where even a single decimal digit would cause high fluctuations, it would be highly troublesome. Test the model on various devices before proceeding.
- The processing of data is a machine process, but creating its dataset is a human process. And as such, some amount of human bias can consciously or unconsciously be blended into it. So, while creating large datasets, it is important that one try and keep in mind all the possible setups possible in the said dataset.