Decision trees can handle:

Which is a major challenge in unsupervised outlier detection?

Which is not a reason to apply dimension reduction?

What is the purpose of normalization in preprocessing?

In outlier detection, what is the trade-off between detecting true anomalies and avoiding false positives? Describe one strategy to balance this trade-off.

What is the additive in a yellow top tube used for microbiology?

Random Forest improves decision trees by:

Which of the following visualizations is best for spotting outliers?

Suppose you trained a classifier with 95% accuracy, but it performs poorly on new data. What could be the cause, and how would you address it?

Which of the following clustering methods does not require specifying the number of clusters in advance?