Advantages and Disadvantages of Decision Trees
Advantages of Decision Trees:
A decision tree is a graphical representation of the possible solutions to a problem under given conditions. It has branches, nodes, leaves, etc. A root node is called an initial node which represents the entire sample.
1. Decision trees require less effort for data preparation during pre-processing.
2. It does not require the normalization of data.
3. Decision tree model is very intuitive So, so it’s very easy to explain to any technical team.
4. Decision tree does not require scaling of data as well.
Disadvantages of Decision Trees:
1. Sometimes, The calculation may be more complex for a decision tree.
2. It often involves higher time complexity to train the model.
3. Decision tree is relatively expensive as the complexity and time taken are more.
4. If the size of the data is too big, the decision tree can’t be used in big data because one single tree may grow a lot of nodes which might result in complexity and leads to overfitting.