If Your Data Is Bad, Your Machine Learning Tools Are Useless
Today, efficiency is all about automation and machine learning. When it comes to machine learning, the quality of data plays a crucial role in determining the quality of the results. Without good quality data, the effects of machine learning can be detrimental to an organization’s progress. The phrase, “garbage-in, garbage-out” holds true in such scenarios. Data quality has an effect on machine learning, not once but twice- when a predictive model is trained and in making future decisions. Effect of Data Quality on Training Predictive Models Predictive models need data during the training phase. This historical data should ideally meet very high quality standards- it should be standardized, accurate, unique, properly labeled and complete. The data being used should also cover the entire range of input needed to develop the predictive learning model. Thus, when it comes to the data being used for predictive models, one must focus on all the criteria for quality control s...