What factors influence the accuracy of predictive analytics models in artificial intelligence?
The accuracy of predictive analytics models primarily depends on the quality and volume of available data, its freshness, and how representative it is of the problem being modeled. Performance is also influenced by the choice of algorithms, the level of feature engineering, available computing power, and how frequently models are retrained. In addition, data bias and preprocessing errors can significantly reduce performance, making data governance and continuous validation essential to maintain reliable predictions.