Choosing between mean and median for imputing null values in machine learning depends on the characteristics of the data and the nature of the feature. Here are some scenarios where you might prefer mean or median imputation:
Normal Distribution (No Outliers):
Scenario: The data in the column follows a normal distribution, and there are no significant outliers.
Choice: Use the mean for imputation. The mean is a good representation of the central tendency in normally distributed data.
Skewed Distribution or Presence of Outliers:
Scenario: The data in the column is skewed, or there are outliers that could significantly affect the mean.
Choice: Use the median for imputation. The median is less sensitive to extreme values and provides a better measure of central tendency in skewed distributions.
Categorical or Ordinal Data:
Scenario: The column contains categorical or ordinal data (non-numeric).
Choice: Depending on the nature of the data, you might use the mode (most frequent value) for imputation in this case. If the data has a meaningful order, you might consider using the median.
Sensitive to Outliers:
Scenario: The feature is sensitive to outliers, and imputing with the mean might distort the representation of the majority of the data.
Choice: Use the median. The median is robust to outliers and is a better choice when extreme values are present.
Missing Data Mechanism:
Scenario: The missing data mechanism is not completely at random, and imputing with the mean could introduce bias.
Choice: Carefully analyze the missing data mechanism. If the missing data is related to the value itself (e.g., people with higher income are less likely to report income), imputing with the median might be a better choice.
Data Distribution Not Known:
Scenario: The distribution of the data is not known or cannot be assumed.
Choice: Consider exploring the data distribution and presence of outliers. If in doubt, using the median is a safer choice as it is less influenced by extreme values.
It's important to note that the decision between mean and median imputation is not always clear-cut, and you may need to experiment with both methods or use domain knowledge to make an informed decision based on the specific characteristics of your dataset. Additionally, other imputation techniques, such as regression imputation or more advanced methods, may be considered depending on the context.
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