Decision Trees in Data Mining

Learn what are decision trees in data mining, their types, components, functions, and advantages of using decision trees in data mining.

What is a Decision Tree in Data Mining?

A decision tree is a type of algorithm that classifies information so that a tree-shaped model is generated. It is a schematic model of the information that represents the different alternatives and the possible results for each chosen alternative. Decision trees are a widely used model because they greatly facilitate understanding of the different options.

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The above example of a decision tree helps to determine if one should play cricket or not. If the weather forecast suggests that it is overcast then you should definitely play cricket. If it is rainy, you should play only if the wind is weak and if it is sunny then you should play if the humidity is normal or low.

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Decision Tree Components

The decision tree is made up of nodes and branches. There are different types of nodes and branches depending on what you want to represent. Decision nodes represent a decision to be made, probability nodes represent possible uncertain outcomes and terminal nodes that represent the final outcome.

On the other hand, the branches are differentiated into alternative branches, where each branch leads to a type of result and, the “rejected” branches, which represent the results that are rejected. The model is characterized in that the same problem can be represented with different trees.

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Types of Decision Trees in Data Mining

Decision tree in data mining is mainly divided into two types –

Categorical Variable Decision Tree

A categorical variable decision tree comprises categorical target variables, which are further bifurcated categories, such as Yes or No. Categories specify that the stages of a decision process are categorically divided.

Continuous Variable Decision Tree

A continuous variable decision tree has a continuous target variable. One example to understand this could be – the unknown salary of an employee can be predicted bases on the available profile information, such as his/her job role, age, experience, and other continuous variables.

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Functions of Decision Tree in Data Mining

For greater precision, multiple decision trees are combined with e 4 assembly methods.

Bagging or Assembly – This method creates several decision trees as a resampling of the source data, and then the tree that denotes that the best results should be used.

Random Jungle Sorter – Multiple decision trees are generated to increase the sort rate and efficiently separate data.

Expanded Trees – Multiple trees are created to correct the errors of the last one with respect to the first.

Random Forest or Rotation Forest – Decision trees created in this scenario are analyzed based on a series of main variables.

Decision Tree Algorithms

Although there are various algorithms used to create decision trees in data mining, the most relevant are the following:

ID3: decision trees with this algorithm are oriented towards finding hypotheses or rules in relation to the analyzed data.

C4.5: decision trees that use this algorithm focus on classifying data, in this way; they are associated with statistical classification.

ACR: the decision trees of this algorithm are focused on avoiding future problems, as they are used to detect the causes that generate the defects.

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Advantages of Using Decision Trees in Data Mining

Decision trees in data mining provide us with various advantages to analyze and classify the data in your information base. However, experts highlight the following –

Ease of Understanding

Because data mining tools can visually capture this model in a very practical way, people can understand how it works after a short explanation. It is not necessary to have extensive knowledge of data mining or web programming languages.

Does Not Require Data Normalization

Most data mining techniques require the preparation of data for processing, that is, the analysis and discard of data in poor condition. This is not the case for decision trees in data mining, as they can start working directly.

Handling of Numbers and Categorized Data

One of the main differences between neural networks and decision trees is that the latter analyze a large number of variables.

While neural networks simply focus on numerical variables, decision trees encompass both numerical and nominal variables. Therefore, they will help you to analyze a large amount of information together.

“White Box” Model

In web programming and data mining, the white box model brings together a type of software test in which the variables are evaluated to determine what are the possible scenarios or execution paths based on a decision.

Uses of Statistics

Decision trees and statistics work hand in hand to provide greater reliability to the model that is being developed. Since each result is supported by various statistical tests, the probability of any of the options analyzed can be known exactly.

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Handles Big Data

Do you have large amounts of information to analyze? With decision trees, you can process them seamlessly. This model works perfectly with big data, as it uses computer and web programming resources to manipulate each point of information.

Conclusion

To improve your processes and be more efficient in the industry, you must add value to your data. With the help of decision tree in data mining, you will be able to carry out an adequate analysis and classification to transform your information into new processes and strategies.

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