Sign up to save your podcasts
Or
Share Outlier Detection: What You Need to Know
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Outlier Detection: What You Need to Know
This story was originally published on HackerNoon at: https://hackernoon.com/outlier-detection-what-you-need-to-know.
Decisions are usually based on the sample mean, which is very sensitive to outliers and can dramatically change the value. So, it is crucial to manage outliers
Check more stories related to data-science at: https://hackernoon.com/c/data-science.
You can also check exclusive content about #outlier-detection, #statistics, #python3, #variance-reducing, #what-is-outlier-detection, #bootstrap, #problem-formulation, #data-analysis, and more.
This story was written by: @nataliaogneva. Learn more about this writer by checking @nataliaogneva's about page,
and for more stories, please visit hackernoon.com.
Analysts often encounter outliers in data during their work. Decisions are usually based on the sample mean, which is very sensitive to outliers. It is crucial to manage outliers to make the correct decision. Let's consider several simple and fast approaches for working with unusual values.
View all episodes
By HackerNoonThis story was originally published on HackerNoon at: https://hackernoon.com/outlier-detection-what-you-need-to-know.
Decisions are usually based on the sample mean, which is very sensitive to outliers and can dramatically change the value. So, it is crucial to manage outliers
Check more stories related to data-science at: https://hackernoon.com/c/data-science.
You can also check exclusive content about #outlier-detection, #statistics, #python3, #variance-reducing, #what-is-outlier-detection, #bootstrap, #problem-formulation, #data-analysis, and more.
This story was written by: @nataliaogneva. Learn more about this writer by checking @nataliaogneva's about page,
and for more stories, please visit hackernoon.com.
Analysts often encounter outliers in data during their work. Decisions are usually based on the sample mean, which is very sensitive to outliers. It is crucial to manage outliers to make the correct decision. Let's consider several simple and fast approaches for working with unusual values.