How can you handle outliers when cleaning data for machine learning?

3 How to handle outliers in numerical data?

When dealing with numerical data, there are several options for handling outliers, depending on the purpose and type of the analysis. The simplest and most straightforward method is to remove outliers, but this can also result in losing valuable information or reducing the sample size. It is important to be confident that the outliers are due to errors or anomalies before removing them. Alternatively, replacing outliers with more reasonable values, such as the mean, median, or a value within the whiskers can reduce their impact on the model. Additionally, transforming outliers by applying a mathematical function can improve the performance of some models that assume normality. Finally, binning outliers into bins or categories based on certain criteria can reduce noise and variability in the data. However, this can also lead to loss of information and introduce arbitrariness in the binning process.

4 How to detect outliers in categorical data?

Categorical data can be either nominal or ordinal. Nominal data can have any number of categories that are not ordered, while ordinal data can have categories that are ordered. One way to detect outliers in categorical data is to use frequency tables, which show the count and percentage of each category. Any category that has a very low or high frequency compared to the others can be considered as an outlier. Another way to detect outliers is to use chi-square tests, which measure the association between two categorical variables. A significant chi-square test indicates that there is a relationship between the variables, and any category that deviates from the expected frequency can be considered as an outlier.

5 How to handle outliers in categorical data?

When dealing with outliers in categorical data, there are several options depending on the context and goal of the analysis. One way is to remove outliers, but this could lead to losing information or reducing sample size. Replacing outliers with more common values, such as the mode or median, can reduce the impact of outliers on the model, but it can also introduce bias. Merging outliers with other categories can reduce the number of categories and simplify analysis, but it can also lose some information and introduce subjectivity. Lastly, creating a new category for outliers such as "other", "unknown", or "missing" can preserve information and acknowledge their existence, but it can also increase complexity and reduce the power of analysis.

6 How to choose the best method for handling outliers?

When it comes to handling outliers, there is no one-size-fits-all answer. Different methods have their own advantages and disadvantages, and the best approach depends on the nature and purpose of the data and the analysis. However, some general guidelines should be followed: always explore and visualize the data before dealing with outliers; justify and document your decisions for handling them; evaluate and compare the results of different methods for handling outliers. Outliers are an expected part of real-world data, but they can be managed effectively with proper methods and best practices, which can then improve data quality and machine learning outcomes.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?