Outliers, or outliers, are values that deviate significantly from the majority of other values in a dataset. In the context of artificial intelligence and machine learning, outliers can be a major problem because they can have a negative effect on the accuracy and effectiveness of machine learning models. Outliers can be the result of measurement errors, data entry errors or rare and infrequent events in the dataset environment. If not handled properly, outliers can bias machine learning models and generate inaccurate or insufficient predictions. Therefore, it is important to identify and deal with outliers in datasets before using them to train machine learning models. Common methods for handling outliers include removing outliers, transforming the data to reduce their impact, and using robust models that are less sensitive to outliers.
Artificial Intelligence (AI) technologies are currently being used in companies to transform business processes, drive innovation and improve the quality of life of their [...]
Read More »Today we are going to talk about how to foresee payment problems and foresee the problems in those customers who are currently not giving it to you. In G [...]
Read More »More and more companies are taking advantage of the relevant information they extract from the data they possess and generate to improve their processes and discover new ways to [...]
Read More »Fraud detection software is an important tool for protecting companies and individuals from fraudulent activity and minimizing the risk of fraud.
Read More »
