Decision Trees ADVANTAGES

DECISION TREES - SIMPLE AND FAST

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 data mining methods 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 formalising their knowledge.
• Decision tree is a white box model. If a given situation is observable in a model the explanation for the condition is easily explained by boolean logic. An example of a black box model is an artificial neural network since the explanation for the results is excessively complex to be comprehended.
• It is possible to validate a model using statistical tests, neural nets and others. 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, decision trees technique is an important data mining method for any scientist dealing with data analysis, no matter if he is a theorist or an expert.

FIELDS 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 or  manager.

In business, decision trees are constructed in order to help with decision  making process.

Decision trees are successfully used to solve real-world problems in the following fields:

• Banking. Estimation of clients’ creditworthiness when giving credits.
• Industry. Production quality control (faults identification), non-destructive tests (like checking weld quality), etc.
• Medicine. Diagnostics of various diseases.
• Molecular biology. Analysis of amino acids composition.

This is by no means full list of the fields where decision trees can be of use.

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