Machine Learning Engineer Question:
Explain me how would you handle an imbalanced dataset?
Answer:
An imbalanced dataset is when you have, for example, a classification test and 90% of the data is in one class. That leads to problems: an accuracy of 90% can be skewed if you have no predictive power on the other category of data! Here are a few tactics to get over the hump:
1- Collect more data to even the imbalances in the dataset.
2- Resample the dataset to correct for imbalances.
3- Try a different algorithm altogether on your dataset.
What’s important here is that you have a keen sense for what damage an unbalanced dataset can cause, and how to balance that.
1- Collect more data to even the imbalances in the dataset.
2- Resample the dataset to correct for imbalances.
3- Try a different algorithm altogether on your dataset.
What’s important here is that you have a keen sense for what damage an unbalanced dataset can cause, and how to balance that.
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