Decision Tree

Summary In the day-to-day operations of Bellsouth's network administrator, you need to make many decisions. Such a decision-making opportunity came out one week ago. The problem is how to handle the main cable installed by the Department of Transportation. In this article we will use a decision tree to decide how to handle this cable. The discussion will include the main factors involved in making the decision and explain how the final decision was made.

Decision tree learning uses entropy to build trees. The construction decision tree is generated by dividing the data set S into multiple subsets according to all possible values ​​of the "best" attribute (ie, a value that minimizes the (combined) entropy of the resulting subset) Begins with a node. Repeat this process recursively until there are no more attributes to split. This precaution is called ID3 algorithm. In binomial and multinomial classification scenarios, cross entropy is the basis of logistic regression and the standard loss function of the neural network. Typically, p is used for a real (or empirical) distribution (ie distribution of training sets), and q is a distribution described by the model. Let's take a binary logical regression as an example. These two classes are labeled 0 and 1, and the logical model assigns probabilities q_ (y = 1) = and q_ (y = 0) = 1 to each input x. This can be concisely written q ∈ {, 1 -}.

We begin with an overview of classification and regression methods from decision trees, one of the most common methods. Decision trees are used not only for machine learning but also for determination of everyday life. The flowchart is actually a visual representation of the decision tree. For example, Higher School of Economics publishes infographics to ease the lives of employees. The following is a brief explanation of the paper published at the institutional portal. Machine learning can be thought of as a simple classifier that determines the appropriate publication type (books, articles, book chapters, preprints, "economics and media high schools") based on content. (Books, pamphlets, articles), types of magazines, types of original publications (scientific journals, minutes) etc.