What is an algorithm for decision-making based on a tree structure

Decision Tree (Decision Tree) is based on the known probability of occurrence of various situations, by forming a decision tree to find the probability that the expected value of the net present value is greater than or equal to zero, evaluate the project risk, and judge its feasibility The decision analysis method is a graphical method that intuitively uses probability analysis.

Because this kind of decision branch is drawn like the branches of a tree, it is called a decision tree. In machine learning, a decision tree is a prediction model that represents a mapping relationship between object attributes and object values. Entropy = How messy the system is, using algorithm ID3, C4.5 and C5.0 spanning tree algorithms use entropy. This measure is based on the concept of entropy in informatics theory. (Recommended learning:web front-end video tutorial)

The decision tree is a tree structure, in which each internal node represents a test on an attribute, and each branch represents a test output. Each leaf node represents a category.

Classification tree (decision tree) is a very commonly used classification method. It is a kind of supervised learning. The so-called supervised learning is to give a bunch of samples, each sample has a set of attributes and a category. These categories are determined in advance, and then through learning, a classifier can be obtained. This classifier can classify new occurrences. Objects are given the correct classification. Such machine learning is called supervised learning.

Composition

□——The decision point is the choice of several possible options, that is, the best option finally selected. If the decision is a multi-level decision, there can be multiple decision points in the middle of the decision tree, and the decision point at the root of the decision tree is the final decision plan.

○——State node represents the economic effect (expected value) of alternatives. By comparing the economic effects of each state node, the best option can be selected according to certain decision-making criteria. The branches derived from the state nodes are called probability branches. The number of probability branches represents the number of possible natural states that may occur. The probability of the occurrence of the state must be noted on each branch.

△——Result node, mark the profit and loss value obtained by each plan under various natural states on the right end of the result node.

A decision tree contains three types of nodes:

Decision nodes: usually represented by rectangular boxes

Opportunity nodes: usually represented by circles Represents the

end point: usually represented by a triangle

Decision tree learning is also a common method in data exploration. Here, each decision tree expresses a tree structure that uses its branches to classify objects of this type depending on their attributes. Each decision tree can rely on a split of the source database for data testing.

This process can recursively prune the tree. The recursive process is complete when no more divisions can be made or a single class can be applied to a branch. In addition, the random forest classifier combines many decision trees to improve the accuracy of classification.

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