Decision Tree
Decision Tree is a data mining method that allows to create easy to
understand and use decision models.
The decisions are represented as the nodes of the tree model. The results of
the decisions are outputed as the final "leafs" of the tree.
Decision Tree Advantages
Amongst other data mining methods, decision tree is the
method that has several advantages:
- Intuitively comprehensible
classification model. People are able to
understand decision tree models after a brief explanation.
- Data preparation for a decision
tree is basic or unnecessary. Other techniques
often require data normalisation, dummy variables need to be created and blank
values to be removed.
- Rules generation in the fields
where experts have difficulties with formalizing their knowledge.
- Is a white box model.
If a given situation is observable in a model the explanation for the
condition is easily explained by a simple boolean logic.
- It is easy to validate a model
using statistical tests and other data mining methods.
That makes it possible to account for the reliability of the model.
- Is robust, perform well with large
data in a short time. Large amounts of data can
be analysed using personal computers in a time short enough to enable
stakeholders to take decisions based on its analysis.
Because of these and many other reasons, methodology of
decision trees is an important tool for any scientist dealing with data
analysis, no matter if he is theorist or practician.
Areas of Decision Trees Application
Decision trees are an excellent tool in decision-making
and data mining systems. They can be of good service to any analyst,
manager or scientist.
Decision trees are successfully used to solve real-world
problems in the following fields:
- Banking.
Estimation of clients creditworthiness when giving credits.
- Insurance Industry.
Detecting possibly fraudulent clients in customers databases.
- Medicine.
Diagnostics of various diseases.
- Molecular biology.
Analysis of amino acids composition.