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Are decision trees good for high dimensional data?
The decision tree predicts almost all the non-event class (90% of the data) with high accuracy, whereas almost entirely misclassifies the event class.
Why decision tree is high variance?
An algorithm like Decision Tree has low bias but high variance, because it can easily change as small change in input variable. In general, it does not generalize the pattern well. It leads to overfitting. It means there is a trade-off between predictive accuracy and generalization of pattern outside training data.
What is decision tree in big data?
A Decision Tree is an algorithm used for supervised learning problems such as classification or regression. Each leaf of the tree is labeled with a class or a probability distribution over the classes. A tree can be “learned” by splitting the source set into subsets based on an attribute value test.
How are decision rules used in a decision tree?
With low-dimensional tabular data, decision rules in a decision tree are simple to interpret e.g., if the dish contains a bun, then pick the right child, as shown below. However, decision rules are not as straightforward for inputs like high-dimensional images.
Which is more accurate decision tree or wideresnet hierarchy?
By contrast, the higher-accuracy WideResNet hierarchy (right) makes more sense, cleanly separating Animal from Vehicle —thus, the higher accuracy, the more interpretable the NBDT. With low-dimensional tabular data, decision rules in a decision tree are simple to interpret e.g., if the dish contains a bun, then pick the right child, as shown below.
Do you need domain knowledge for decision tree classifier?
The construction of decision tree classifier does not require any domain knowledge or parameter setting, and therefore is appropriate for exploratory knowledge discovery. Decision trees can handle high dimensional data. In general decision tree classifier has good accuracy.
What are the weaknesses of decision tree methods?
The weaknesses of decision tree methods : Decision trees are less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute. Decision trees are prone to errors in classification problems with many class and relatively small number of training examples.