What kind of bias might be introduced into a model if missing data is not appropriately addressed?
- All above.
- Confirmation bias.
- Observation bias.
- Sampling bias.
Inappropriate handling of missing data can lead to sampling bias, where the model is trained on a non-representative subset of the data, hence the model's predictions could be biased.
Loading...
Related Quiz
- Which Python library is specifically useful for creating interactive plots?
- How does the Variance Inflation Factor (VIF) quantify the severity of Multicollinearity in a regression analysis?
- When applying multiple imputation, increasing the number of imputations can help reduce the ____________.
- Which measure of central tendency is calculated by adding all the numbers and dividing by the number of numbers?
- What is the biggest challenge in the 'wrangle' phase of the EDA process?