Decision Tree Help
A decision tree is a diagram that shows the possible outcomes of an event based on a series of choices. It allows organizations and individuals to weigh possible actions based on their benefits, probabilities, costs, and other factors. As the name suggests, decision trees use tree-like models to make decisions. They are commonly applied in data mining to derive strategies that help businesses reach their goals and in machine learning as well. Basically, a decision tree is like a tree drawn upside down, with one node that braches into many possible outcomes. Every outcome then branches into other nodes representing other possibilities and outcomes. This is what gives a decision tree its tree-like structure. There are three primary types of nodes used in decision trees:
- Chance nodes: These are symbolized by a circle and show the possibility of achieving certain results.
- Decision nodes: These are denoted by a square and shows the decision which is to be made
- End nodes: These show the final result of a given decision path
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How To Draw An Effective Decision Tree
To draw a decision tree, the first thing you need to do is choose a medium. It can be a whiteboard where you will draw the tree by hand or special decision tree software with inbuilt tools and functions to make the drawing easier. Whichever medium you pick, below are the steps you should follow:
- Main decision: Start by drawing a small box that represents the main decision. Then from the box, draw a line to the right of every possible action or solution and label the each line accordingly.
- Chance and decision nodes: Expand the tree by adding chance and decision nodes as follows:
- If there is another decision to be made, draw another box
- If the outcome of a decision is not known, draw a circle
- If the problem has already been solved, leave the tree blank for the time being
From every decision node you have added, draw all possible solutions. Also, draw lines that represent the possible outcomes from each chance node.
- End point: Continue to expand your tree until all the lines have reached an end point. An end point means that there are no more chance outcomes to consider and more choices to be made. End points are represented with triangles.
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Decision Trees And Machine Learning
As mentioned earlier, decision trees are also widely used in machine learning. They are used to develop automated predictive models, which are in important part of machine learning, statistics, and data mining. The application of decision trees in machine learning is often referred to as decision tree learning and takes into account the observations about a certain item to make forecasts about the value of the item.
In decision tree learning, nodes do not represent decisions. Instead, they represent data. Each branch includes a set of classification rules (decision rules) or attributes that are linked to a specific class label found towards the end of the branch. Decision rules are usually expressed using “if”, “then” clauses.
The more data is added to decision learning models, the more accurate the predictions become. Data scientists sometimes use multiple decision trees in one model to make the outcomes even more accurate. Using decision tree models in machine learning has been found to be advantageous in several ways. For instance:
- Problemscan be modeled with multiple outputs
- The cost of predicting data using decision trees decreases as more data points are added to the tree
- Works for both numerical and categorical data
- Decision tree learning uses while box models which makes the results easy to interpret
- In machine learning, the reliability of the decision tree can be easily tested and quantified
- Decision trees in machine learning tend to be accurate irrespective of whether they violate the assumption of initial (source) data or not.
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Decision Tree Terminology
If you are just getting started with decision trees, it is important that you familiarize yourself with some of the terms used here so that you can better handle the subject. We will get you acquainted with the most common. Read on!
- Root node: A root node represents the entire decision, sample, or population. It can further be divided into two or multiple homogenous sets.
- Splitting: This is simply dividing a given node into multiple sub-nodes.
- Decision node:When you further split a sub-node into sub-notes, then the parent sub-note becomes a decision node.
- Leaf or terminal node: A node that does not split is referred to as a leaf or terminal node.
- Pruning: This is the process of removing sub-nodes from a decision node. It is simply the opposite of splitting.
- Sub–tree or branch: This is a sub-section of the entire decision tree.
- Parent node: This is the node from which all sub-nodes originate.
- Child node: A child node is any node that originates from the parent node.
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Applications Of Decision Trees
Decision trees can be applied to a number of data analyses actions including:
- When the user is trying to achieve a certain objective; for instance, finding ways to achieve maximum profit, minimize costs, or improve productivity
- When there are many decisions to be made and causes of actions to be undertaken
- When the user wants to know the advantages and disadvantages of each action taken in decision making
- When the outcomes are not certain
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Advantages Of Decision Trees
There are a number of reasons why decision trees are so popular in data handling today:
- They are easy to understand
- They hasten the process of data manipulation
- They require less data cleaning
- They can handle both categorical and numerical data
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