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How does decision trees handle imbalanced data?
Decision trees frequently perform well on imbalanced data. They work by learning a hierarchy of if/else questions and this can force both classes to be addressed. While our accuracy score is slightly lower, both F1 and recall have increased as compared to logistic regression!
Can decision trees be used only for binary classification tasks?
Can decision tree be used for classification tasks?(single option) a)yes but only for binary classification tasks.
What type of data is best for decision trees?
Decision trees are used for handling non-linear data sets effectively. The decision tree tool is used in real life in many areas, such as engineering, civil planning, law, and business.
Can a decision tree be used for imbalanced classification?
The decision tree algorithm is effective for balanced classification, although it does not perform well on imbalanced datasets. The split points of the tree are chosen to best separate examples into two groups with minimum mixing.
When to use a non binary decision tree?
Regarding uses of decision tree and splitting (binary versus otherwise), I only know of CHAID that has non-binary splits but there are likely others. For me, the main use of a non binary split is in data mining exercises where I am looking at how to optimally bin a nominal variable with many levels.
Why is unbalanced dataset important in binary classification?
This is a well-known phenomenon in binary classification (and it extends naturally to multi-class settings). This is an important issue, because an imbalanced dataset may lead to inflated performance estimates. This in turn may lead to false conclusions about the significance with which the algorithm has performed better than chance.
Can a training set lead to a biased classifier?
This is an interesting and very frequent problem in classification – not just in decision trees but in virtually all classification algorithms. As you found empirically, a training set consisting of different numbers of representatives from either class may result in a classifier that is biased towards the majority class.