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

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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.

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