Decision Trees in R
A decision tree is a versatile machine learning algorithm used for both regression and classification tasks. It utilizes a set of predefined binary rules to classify categorical target variables and continuous target variables. Hence, it can also be referred to as a Classification and Regression Tree (CART). Decision trees are used in R for data analysis to ensure effective results. Below is all you need to know about decision trees in R.
Features of a decision tree
There are several elements that make up a decision tree. Our decision trees in R assignment help experts have listed them below:
- Root node: The root node represents the entire sample or population being studied. It further divides into multiple homogenous sets of nodes.
- Splitting: Also known as dividing, splitting is the process of separating one node into multiple sub-nodes.
- Decision node: This type of node is obtained when a sub-node divides further into multiple sub-nodes.
- Leaf: This term is used to describe a node that does not split. Such nodes are also called terminal nodes.
- Pruning: The process of removing sub-nodes from a decision tree is called pruning. Its opposite is ‘splitting’.
- Branch: The branch is a sub-section of the entire decision tree.
- Parent node: This is a node that is divided into multiple sub-nodes. The resulting sub-nodes are referred to as children.
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Why decision trees are an essential element in R
Decision trees are very easy to learn, use, and explain. Compared to other classification and regression techniques that one can perform in R, decision trees can mirror human decision making better than any of these approaches. Not only that. They can be represented graphically and can handle qualitative predictors more easily without having to create dummy variables.
However, like all classification and regression methods, decision trees have their downside, the most common being reduced accuracy. Generally, decision trees are not quite robust and hence they do not display the same degree of predictive accuracy as the rest of the approaches. A small alteration in data can result in massive changes in the final results. However, by combining several decision trees using techniques such as boosting, bagging, and random forests, the predictive accuracy and performance of a decision tree can be largely improved. For more information about the upside and downside of decision trees, collaborate with our decision trees in R assignment help professionals.
Types of decision trees in R
There are two major types of decision trees in R programming. These include:
- Categorical variable decision tree: This type of decision tree consists of target variables that are classified into categories. For instance, the categories in question can be “yes” or “no”. This type of decision tree simply means that a decision has to fall into one category, not in-between.
- Continuous variable decision tree: As the name suggests, this is a decision tree where the target variables are continuous. To understand this type of decision tree, let’s consider an example where we are having trouble determining whether a certain customer will renew his premium with his insurance company or not (the decision could be yes, no or maybe). Here, we understand that the customer’s income is a major factor but the insurance company probably doesn’t have the details of its customers’ income. To predict the customer’s income, we need to create a decision tree based on the customer’s occupation, age, and other variables. In other words, we are estimating values for a continuous variable.
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