Suppose, you found that your model is suffering from high variance. Which algorithm do you think could handle this situation and why?

Handling High Variance

• For handling issues of high variance, we should use the bagging algorithm.
• Bagging algorithm would split data into sub-groups with replicated sampling of random data.
• Once the algorithm splits the data, we use random data to create rules using a particular training algorithm.
• After that, we use polling for combining the predictions of the model.