We can say that now is the era we live in the era of big data, we strive to do to get useful knowledge from the data to be used in the future to life in the era of big data, which is inseparable from the data mining technology. The so-called data mining is not mine large amounts of data, but useful data mining, like mining, we must find a data we need, which uses knowledge of the decision tree.
1. Status of decision tree
Now, companies are gradually beginning to use data mining techniques, through the relevant data analysis can reduce costs, improve efficiency, develop new products and make more informed business decisions. The data mining technology, there are many, such as cluster analysis, such as a decision tree. In general, how to tap data value, and the related impact analysis data generated primarily by statistical methods, the use of machine learning algorithms. Targeted training and learning with a large number of samples, confirmed until a more reasonable model. A decision tree is a basic method of machine learning algorithms for the problem of classification, here we give you talk about the tree of knowledge.
2. The concept of the decision tree
Decision tree is a kind of never order, random sample dataset inferred tree representation of the method of classification rules. It uses a recursive top-down manner, in comparing the attribute values and internal nodes of the decision tree, the leaf nodes in the decision tree branch down from the conclusion of the node is determined according to different attribute values. Thus the path from the root node to a leaf node corresponds to a rule, whole grain corresponds to a set of decision trees expression rules. Because of this decision branches painted graphics like branches of a tree, it is called a decision tree. In machine learning, decision tree is a predictive model, he represents a mapping relationship between the object and the object attribute value.
Step 3. Decision Tree Algorithm
Decision tree algorithm is divided into two steps: First, the generated tree, start at the root node all the data, then the recursive data slice; Second pruning tree, is to remove some of the noise, or the data may be abnormal. These steps each step is very important, we do data mining, decision tree when in use must not ignore these issues.
In this article we introduce the knowledge about data mining decision tree for everyone. In data mining, decision tree is a very important body of knowledge, but also can not ignore knowledge, I hope this article will help you better understand the learning and data mining.