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Should I remove outliers from training data?
Outliers badly affect mean and standard deviation of the dataset. These may statistically give erroneous results. So it is desirable to detect and remove outliers. They can also impact the basic assumption of Regression, ANOVA and other statistical model assumptions.
Under which circumstances would it be appropriate to remove outlying data points?
Answer: If an outlying data point causes the analysis and conclusion of a scientific study to become error then it would be appropriate to remove the underlying data points from the analysis and conclusion in a scientific study.
How to get rid of outliers in a data set?
Trim the data set. Set your range for what’s valid (for example, ages between 0 and 100, or data points between the 5th to 95th percentile), and consistently delete any data points outside of the range. Trim the data set, but replace outliers with the nearest “good” data, as opposed to truncating them completely.
When do you remove outliers in machine learning?
If you decided to remove outliers. Please remove them before the split (even not only before a split, it’s better to do the entire analysis (stat-testing, visualization) again after removing them, you may find interesting things by doing this). If you remove outliers in only any one of train/test set it will create more problems.
When is the best time to remove outliers?
If you decided to remove outliers. Please remove them before the split (even not only before a split, it’s better to do the entire analysis (stat-testing, visualization) again after removing them, you may find interesting things by doing this).
Why do we need to know about outliers?
Outliers can be very informative about the subject-area and data collection process. It’s essential to understand how outliers occur and whether they might happen again as a normal part of the process or study area. Unfortunately, resisting the temptation to remove outliers inappropriately can be difficult.