[amp_mcq option1=”Outlier” option2=”Anomaly” option3=”Null Value” option4=”Feature” correct=”option3″]<\/p>\n
The correct answer is: C. Null Value<\/p>\n
A null value is a data point that is missing a value for one or more features. It is often represented by a special value such as “NULL” or “NA”. Null values can occur for a variety of reasons, such as when a data point is incomplete, when a measurement is not available, or when a value is not applicable.<\/p>\n
Outliers are data points that are significantly different from the rest of the data. They can be caused by errors in data collection or processing, or they may represent genuine but unusual observations. Anomalies are similar to outliers, but they are often defined as data points that are inconsistent with the expected behavior of the data.<\/p>\n
Features are the variables that are used to describe the data. They can be continuous, such as height or weight, or they can be categorical, such as gender or eye color.<\/p>\n
I hope this helps! Let me know if you have any other questions.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”Outlier” option2=”Anomaly” option3=”Null Value” option4=”Feature” correct=”option3″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[734],"tags":[],"class_list":["post-45466","post","type-post","status-publish","format-standard","hentry","category-introduction-to-data-science","no-featured-image-padding"],"yoast_head":"\n