April 2020
Intermediate to advanced
330 pages
7h 44m
English
Content preview from Hands-On Deep Learning with R
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Handling missing values
In the preprocessing work that we just completed, we decided to remove missing values. This is an option when there are very few cases that contain missing values and, in this example, this was true. However, other situations may require different approaches to handling missing values. Here are some common options in addition to deleting rows and columns:
- Imputation with a measure of centrality (mean/median/mode): Use one of the measures of centrality to fill in the missing values. This can work well if you have normally distributed numeric data. Modal imputation can also be used on non-numeric data by selecting the most frequent value to replace the missing values.
- Tweak for the missing values: You can use the known ...
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