What is a Decision Tree in Data Mining?
A decision tree in data mining is a type of algorithm that classifies information and produces a tree-like model. It is a schematic model of information that represents different options and possible outcomes for each option selected. Decision trees are a widely used model because they make it much easier to understand different options.
Decision Tree Components
A decision tree consists of nodes and branches. There are different types of nodes and branches, depending on what you want to represent. Decision nodes represent decisions to be made; probability nodes represent possible uncertain outcomes; and final nodes represent final outcomes.
On the other hand, branches are differentiated into alternative branches, where each branch leads to one type of outcome, and “rejected” branches, which represent rejected outcomes. A feature of this model is that the same problem can be represented by different trees.
Types of Decision Trees in Data Mining
Decision trees in data mining are mainly divided into two types:
Categorical Variable Decision Tree
A categorical variable decision tree has a categorical target variable that is another branching category, such as yes or no. Categories specify that the steps of the decision-making process are clearly divided.
Continuous Variable Decision Tree
A continuous-variable decision tree has a continuous target variable. An example of how to understand this is that an employee’s unknown salary can be estimated based on available profile information such as the employee’s job, age, experience, and other continuous variables.
Functions of Decision Tree in Data Mining
Combine multiple decision trees with four assembly methods to increase accuracy.
Bagging or assembly: This method creates as many decision trees as possible by re-sampling the source data to create a tree that tells you which result is best to use.
Random Forest Sorter: Generate multiple decision trees to increase the sorting rate and separate data efficiently.
Spanning tree: Multiple trees are created to correct errors in the final tree relative to the first tree.
Random Forest or Rotational Forest: In this scenario, the decision tree created is analyzed based on a set of key variables.
Decision Tree Algorithms
There are many different algorithms used to create decision trees in data mining, but the most relevant are:
ID3: The purpose of decision trees using this algorithm is to find hypotheses and rules related to the analyzed data.
C4.5: Decision trees using this algorithm focus on classifying data in this way. They are related to statistical classification.
ACR: The decision tree of this algorithm is used to find the causes that cause faults, so it focuses on avoiding future problems.
Advantages of Using Decision Trees in Data Mining
Data mining decision trees provide a variety of benefits for analyzing and classifying data in an information base. However, experts emphasize the following points:
Easy to understand
Data mining tools can display this model in a very practical way, so you can understand, with a simple explanation, how the model works. No extensive knowledge of data mining or web programming languages is required.
No data normalization required
Most data mining techniques require preparing data for processing, which means analyzing and discarding bad data. This is not the case, because data mining decision trees can start working right away.
Handling numerical and classification data
The main difference between neural networks and decision trees is that the latter analyze a larger number of variables.
Neural networks focus solely on numerical variables, whereas decision trees include both numerical and nominal variables. Therefore, it helps in analyzing large amounts of information simultaneously.
“White Box” Model
In web programming and data mining, white box models put together a type of software testing where variables are evaluated to determine possible scenarios or execution paths based on a decision.
Use of statistics
Decision trees and statistics work together to increase confidence in the models you are developing. Each result is supported by various statistical tests, so you know exactly the probability of the analyzed option.
Handle big data
Do you have a large amount of information to analyze? Decision trees allow you to process it seamlessly. This model works perfectly with big data because it uses computer and web programming resources to manipulate each point of information.
Decision Tree in Machine Learning
Machine learning decision trees are versatile and interpretable algorithms used for predictive modeling. It structures decisions based on input data, making it suitable for both classification and regression tasks. This article takes a closer look at the components, terminology, structure, and benefits of decision trees and explores their applications and learning algorithms.
Decision trees are a type of supervised learning algorithm commonly used in machine learning to model and predict outcomes based on input data. It is a tree-like structure where each internal node tests an attribute, each branch corresponds to an attribute value, and each leaf node represents a final decision or prediction. Decision tree algorithms fall under the category of supervised learning. These can be used to solve both regression and classification problems.
Decision tree terminology
Terminology related to decision trees refers to various components and aspects of the tree structure and the decision-making process.
- Root Node: The root node of the decision tree represents the option or feature from which the tree branches and is the topmost node.
- Internal node (decision node): A node in a tree whose choice is determined by the value of a particular attribute. These nodes have branches that point to other nodes.
- Leaf node (terminal node): the end of a branch where a selection or prediction is made. Leaf nodes no longer have branches.
- Branches (edges): links between nodes that show how decisions are made based on a particular situation.
- Partitioning is the process of dividing a node into two or more subnodes based on decision criteria. This involves selecting features and boundaries to create subsets of the data.
- Parent node: the node that splits into child nodes. The parent node from which to split.
- Child node: a node created as a result of splitting from the parent node.
- Decision criteria: rules or conditions used to determine how data is partitioned at a decision node. It involves comparing a range of feature values.
- Pruning is the process of removing branches or nodes from the decision tree to improve generalization and prevent overfitting.
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