Using Decision trees in improving Machine Learning Models
Abstract
Since the beginning of civilization, the two oldest sciences, from which all other sciences were founded, were mathematics and linguistics. The reason behind this was the basic human need to count what was around him and to express and memorize numbers. Since that time, the saying “mathematics is the mother of sciences and the language of the universe” arose, but the development of pure sciences and the good it brought to all of humanity has tempted scientists and policy-makers to pay more attention to it than mathematics and language. These two sciences are developing quietly and silently, away from the spotlight. And the journey of their silent evolution continued until the fates came by a strange coincidence. It was the discovery of other than the life of mankind, which is the computer. And it penetrates into all walks of life. And because it was built on the basis of digital counting systems, it blended distinctly with mathematics and its derivatives. This research examines a very important concept in the science of operations research, which is decision trees. And how to use it in machine learning applications. Evidence of saving effort, time and possibilities for the machine learning model to reach the best results in the least possible time and cost.
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