If there are outliers in the independent variables, we can either delete them or deal with them using a variety of strategies, such as feature scaling, imputation, binning and trimming. If there are outliers in an independent variable, it does not matter much. However, the target variable is a crucial component of our dataset. and if we eliminate the outliers from the target variable, we lose all of our useful information, including predictions. In our dataset, the target variable has a significant impact. What will we do in this case if our dependent or target variable has outliers?
What kind of effects does it show on models if outliers are present in target variables?
What are the methods used to remove outliers from target variables?
Do outliers impact the model-building process if they are present in target variables?
If we remove outliers from the target variable, what happens to our model?
