Contents
Can decision trees be used for continuous data?
Types of decision trees are based on the type of target variable we have. It can be of two types: Continuous Variable Decision Tree: Decision Tree has a continuous target variable then it is called Continuous Variable Decision Tree.
How do you choose between logistic regression and decision tree?
Decision Trees bisect the space into smaller and smaller regions, whereas Logistic Regression fits a single line to divide the space exactly into two.
Which of the following algorithm can handle continuous data for decision tree?
Unlike ID3, it can handle both continuous and discrete attributes very efficiently and after building a tree, it undergoes pruning by removing all the branches having low importance. CART can perform both classification and regression tasks and they create decision points by considering Gini index unlike ID3 or C4.
What is the biggest weakness of decision trees compared to logistic regression trees?
What is the biggest weakness of decision trees compared to logistic regression classifiers? Decision trees are more likely to overfit the data since they can split on many different combination of features whereas in logistic regression we associate only one parameter with each feature.
Is it better to use a decision tree or logistic regression?
In the example presented in this article, the differences between decision tree and 2nd logistic regression are very negligible. However, in real life, when working on un-polished data, combining decision tree with logistic regression may produce far better results.
What are the advantages of logistic regression in machine learning?
Logistic regression is one of the most used machine learning techniques. Its main advantages are clarity of results and its ability to explain the relationship between dependent and independent features in a simple manner. It requires comparably less processing power, and is, in general, faster than Random Forest or Gradient Boosting.
How is Woe different from a decision tree?
The main difference is that WoE is built separately for each feature, while nodes of decision tree select multiple features at the same time.
Which is faster random forest or logistic regression?
It requires comparably less processing power, and is, in general, faster than Random Forest or Gradient Boosting. However, it has also some serious drawbacks and the main one is its limited ability to resolve non-linear problems.