Contents
Can a decision tree handle non-linear data?
Decision trees is a non-linear classifier like the neural networks, etc. It is generally used for classifying non-linearly separable data. Even when you consider the regression example, decision tree is non-linear.
Can Random Forest handle non-linear?
A Random Forest’s nonlinear nature can give it a leg up over linear algorithms, making it a great option. However, it is important to know your data and keep in mind that a Random Forest can’t extrapolate. It can only make a prediction that is an average of previously observed labels.
Why is linear regression better than random forest?
If the dataset contains features some of which are Categorical Variables and some of the others are continuous variable Decision Tree is better than Linear Regression,since Trees can accurately divide the data based on Categorical Variables.
How is a decision tree a non-linear mapping?
A decision tree is a non-linear mapping of X to y. This is easy to see if you take an arbitrary function and create a tree to its maximum depth. if x = 1, y = 1 if x = 2, y = 15 if x = 3, y = 3 if x = 4, y = 27 Of course, this is a completely over-fit tree and won’t generalize. But it demonstrates why a decision tree is a non-linear mapping.
What are the drawbacks of a decision tree?
While the tree is able to classify dataset that is not linearly separable, it relies heavily on the quality of training data and its accuracy decreases around decision boundaries. One way to address this drawback is feature engineering.
How are decision trees used in ML algorithms?
As opposed to other ML algorithms that suffer from missing data, Decision Trees can very well handle it. With the help of CHAID, Decision Trees can handle missing variables by treating them as an isolated category or merging them into another. Trees like CART and C5.0 allow the variables to be handled directly.
How to get rid of outliers in a decision tree?
There are two main approaches to solve this problem: either remove the outliers or build your own decision tree algorithm that makes splits based on the median instead of the average, as the median is not affected by outliers.