Data and Algorithm are the two most important components required for machine learning. The question arise here which one is more important in terms of quality — a good data or good algorithm can only help to develop the efficient AI models to work with best results.
Actually, both — Good Data and Good algorithm are very much important to develop machine learning based models. But right here question is which is one is more important ?
Suppose you have best quality or say a Good quality of data but algorithm is not suitable or not able to use or implement the data sets correctly to give the predictive results. An algorithm for machine learning should utilize the data in the right manner and if algorithm is not capable to do so, it’s worthless to feed the quality data sets and expect positive outcomes.
While one the other hand, if the algorithm is good but you have unreliable or not suitable training data sets and you are using it for machine learning it will definitely give you the unsatisfying results or answer for the same questions could be varied due to irrelevant data sets. A data should be accurate and collected or classified from the reliable sources.
Actually, the quality also depends the type of machine learning model you are working. There are few simple models that only needs huge amount of quality datasets and algorithm is not so important because such models don’t need to do lots of analysis work instead of giving the answers in respect of a particular question asked by the users.
However, if you are working on a critical machine learning model you also need a good quality of algorithm to eat the data correctly and give the best response. But at the same time data is also important to get the best results that can come out with combination of good quality data and good quality algorithm for machine learning and artificial intelligence based applications.
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