Data cleaning. Data cleaning. Data cleaning. You know this, and I don't have to tell you that the quality of your training and validation datasets are key. What I do want to tell you is that we have added Outlier handling capabilities to the platform. Now, how about that?
Visualize and manage your outliers on platform
So, what are outliers and why are they important to stay alert on?
An outlier is a datapoint in your dataset that, for any reason, has a value that is far from the main group. Outliers are not uncommon, and can for example be caused if the value was entered incorrectly by mistake, if the value was collected during an unusual circumstance or if the value was a result of natural variation. Either way, the outlier represents a diversity in your dataset that does not represent the norm.
In order for a model to perform well, the quality of the data it is trained on is of utmost importance. Outliers will affect model performance negatively and you might end up with a badly performing model.
You can learn more about how to handle outliers on the Peltarion platform by visit this page.
Happy data cleaning!