What are the decision tree classification algorithms?

Question 1: What is the classification of decision tree algorithm? Decision tree algorithm is a method to approximate discrete function values. This is a typical classification method. Firstly, the data is processed, and readable rules and decision trees are generated by inductive algorithm, and then the new data is analyzed. In essence, decision tree is a process of classifying data through a series of rules.

Decision tree method was first produced in 1960s, and reached the end of 1970s. ID3 algorithm was proposed by J Ross Quinlan to reduce the depth of the tree. However, the research on the number of blades has been neglected. Algorithm C4.5 is improved on the basis of ID3 algorithm, and has made great improvements in the treatment of missing values of predictive variables, pruning skills, derivation rules and so on, which is suitable for both classification problems and regression problems.

Decision tree algorithm finds the classification rules contained in data by constructing decision tree. How to construct a decision tree with high precision and small scale is the core content of decision tree algorithm. The construction of decision tree can be divided into two steps. The first step is the generation of decision tree: the process of generating decision tree from training sample set. Generally speaking, the training sample data set is a historical and comprehensive data set, which is used for data analysis and processing according to actual needs. Step 2: Pruning the decision tree: Pruning the decision tree is a process of checking, revising and revising the decision tree generated in the previous stage, which mainly uses the data in the new sample data set (called test data set) to check the preliminary rules generated in the process of generating the decision tree and cut off those branches that affect the accuracy of pre-balance.

Question 2: Data mining classification methods Decision trees can be divided into many types. Data mining, also known as knowledge discovery in database, is a process of intelligently and automatically extracting some useful, credible, effective and understandable patterns from massive data. Classification is one of the important contents of data mining. At present, classification has been widely used in many fields, such as medical diagnosis, weather forecast, credit confirmation, customer differentiation and fraud screening. There are many classification methods.

1, the intuitive representation of decision tree classification can be easily converted into a standard database query.

2. The method of decision tree classification and induction is effective, especially suitable for large data sets.

3. In the process of classification, the decision tree does not need any additional information except the information already contained in the data set.

4. The decision tree classification model has high accuracy. Firstly, the evaluation method of classification model is studied. On this basis, the classification method of decision tree is studied emphatically, and the scalability of decision tree algorithm is analyzed in detail. Finally, the application of decision tree classification and prediction based on OLE DB is given.

Question 3: What are the differences between rule-based classifiers (such as RIPPER algorithm) and decision trees, and what are the differences in usage scenarios? Decision tree is actually a rule classifier. The proposer of error-driven learning method based on transformation demonstrated this problem in his paper. His learning method is a rule learner, but it is equivalent to a decision tree.

Question 4: What are the advantages and disadvantages of decision trees? Decision tree is a decision analysis method to calculate the probability that the expected value of net present value is greater than or equal to zero, evaluate the project risk and judge its feasibility on the basis of knowing the probability of various situations. It is a graphical method that uses probability analysis intuitively.

Advantages and disadvantages of decision tree:

Advantages:

1) can generate understandable rules.

2) The calculation amount is relatively small.

3) It can handle continuous and various text threading.

4) Decision trees can clearly show which fields are more important.

Disadvantages:

1) Continuous fields are difficult to predict.

2) For data with time series, a lot of preprocessing work is needed.

3) If there are too many categories, the error may increase faster.

4) When general algorithms are used for classification, only one field is used for classification.

Question c4.5 how does the decision tree algorithm get the classification result? Decision tree mainly has ID3, C4.5, CART and other forms. ID3 selects the attribute of information gain for recursive classification, and C4.5 is improved to use the information gain rate to select the classification attribute. CART is the abbreviation of classification and regression tree. It shows that CART can not only be classified, but also regressed.

Question 6: The application field of decision tree classification algorithm should not be generalized to economic, social and medical fields, but should be specific to practical problems. And what software is more convenient to implement. Decision tree algorithm is mainly used in data mining and machine learning. Data mining is to find rules from massive data. A famous example is the example of beer and diapers, which is a typical data mining. Decision tree algorithms include ID3, C4.5, CART and so on. All kinds of algorithms use massive data to generate decision trees, which can help people or machines make decisions. The simplest example is that you go to see a doctor. According to the decision tree, the doctor can judge what disease it is. Software can use VISUAL STUDIO, C language, C++, C# and java.

Question 7: The difference between Bayesian network and Bayesian classification algorithm Bayesian classification algorithm is a statistical classification method, which uses probability and statistical knowledge to classify. On many occasions, naive Bayes (Na? Ve Bayes (NB) classification algorithm can be compared with decision tree and neural network classification algorithm. This algorithm is suitable for large databases, with simple method, high classification accuracy and high speed.

Bayesian theorem assumes that the influence of an attribute value on a given category is independent of the values of other attributes, but it is often not true in actual situations, so its classification accuracy may decline. Therefore, many Bayesian classification algorithms, such as TAN(tree augmented Bayes network) algorithm, are used to reduce the assumption of independence.