Empty values, also known as null values or missing values, are values that have no defined value in a data set. Empty values can arise for a number of reasons, such as missing information, data deletion or data corruption.
In machine learning, empty values can be a major problem because many machine learning algorithms cannot handle empty values. The presence of empty values can cause errors in data analysis and prediction. In addition, removing records containing empty values can significantly reduce the size of the dataset and affect the performance of the model.
There are different techniques for handling empty values in machine learning, such as removing records with empty values, imputing values, assigning default values, and modelling empty values as a separate feature. The choice of the appropriate technique will depend on the specific problem and the number and distribution of empty values in the dataset.
Once the basic concepts for building a commercial software with artificial intelligence are clear, where it is defined to whom to dedicate effort and [...]
Read More »Deep learning translates as deep learning and is a type of artificial intelligence (AI) that is encompassed within machine learning.
Read More »The fad coming from the USA that will force the incorporation of AI in the process Surely it is only recently that we have started to hear a new concept in [...]
Read More »In this article we are going to focus on how artificial intelligence (AI) can increase efficiency and reduce costs for your company by [...]
Read More »