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.
Blockchain technology is best known as the computer architecture on which Bitcoin and other cryptocurrencies are based, and it is also known as the [...]
Read More »Artificial intelligence (AI) can change the way sales channels and customers are managed for manufacturers and distributors of consumer products, and can [...]
Read More »Artificial intelligence (AI) and machine learning (ML) are two of the most popular technologies used to build intelligent systems for the [...]
Read More »In recent years, all topics related to Artificial Intelligence (AI) have been arousing enormous interest. Perhaps it is because the heart of [...]
Read More »
