How are rare events used in machine learning?
Machine learning commonly requires the use of highly unbalanced data. When detecting fraud or isolating manufacturing defects, for example, the target event is extremely rare – often way below 1 percent. So, even if you’re using a model that’s 99 percent accurate, it might not correctly classify these rare events.
When to use focused data selection in machine learning?
A lot of data scientists frown when they hear the word sampling. I like to use the term focused data selection, where you construct a biased training data set by oversampling or undersampling. As a result, my training data may end up slightly more balanced, often with a 10 percent event level or more (See Figure 1).
How does undersampling help a machine learning algorithm?
As a result, my training data may end up slightly more balanced, often with a 10 percent event level or more (See Figure 1). This higher ratio of events can help the machine learning algorithm learn to better isolate the event signal. For reference, undersampling removes observations at random to downsize the majority class.
Can a ML algorithm misclassify a rare event?
To a certain degree, our rare event question with one minority group is also a small data question: the ML algorithm learns more from the majority group and may easily misclassify the small data group. Here are the million-dollar questions: For these rare events, which ML method performs better?
How to classify rare events in data science?
1. Importation, Data Cleaning, and Exploratory Data Analysis Let’s load and clean the raw dataset. It appears to be tedious to clean the raw data as we have to recode missing variables and transform qualitative into quantitative variables. It takes even more time to clean the data in the real world.
Is there a problem with logistic regression for rare events?
Although King and Zeng accurately described the problem and proposed an appropriate solution, there are still a lot of misconceptions about this issue. The problem is not specifically the rarity of events, but rather the possibility of a small number of cases on the rarer of the two outcomes.