matlab decision tree example

The Microsoft Decision tree algorithm is a classification and regression algorithm provided by Microsoft SQL Server Analysis Services for predictive modeling of discrete and continuous attributes.For discrete attributes, the algorithm predicts the relationships between the input columns in the dataset. It uses the values of these columns (also called states) to predict the state of a column that is specifie

Decision tree is a very basic classification and regression method, but as the previous blog machine learning sorting algorithm: Ranknet to Lambdarank to Lambdamart in the Lambdamart algorithm, the most basic algorithm is a lot of classic, complex, The basis of an efficient machine learning algorithm. About what is a decision

8.4.3 C # Decision TreeIn the fifth chapter, we discuss the relationship between the differential union of F # and the class hierarchy in C #. In this example, we will use another kind of hierarchy to represent the nodes of the decision tree, and derive two additional classes to represent the two different cases (the f

Label: nyoj129 Decision time limit of tree: 1000 MS | memory limit: 65535 kb difficulty: 4 Description A tree is a well-known data structure that is either empty (null, void, nothing) or is a set of one or more nodes connected by directed edges between nodes satisfying the following properties.There is exactly one node, called the root, to which no di

It is particularly emphasized that the decision tree of the binary lookup is a tree of balance . Generally for an ordered sequence of binary lookup process, need to start from the middle node to compare, so it will go to the left subtree or right sub-tree to compare, so as long as the root of the

Xiao Ding-Dong robot, decision tree, iask, guess 20 (http://y.20q.net/anon) Xiao Ding-Dong robot, decision tree, iask, guess 20 (http://y.20q.net/anon) In the morning, a friend of mine told me that the robot was stupid and not fun. This is why I realized that the robot has not been updated for about half a yea

splitdataset (dataset,axis,value): retdataset = [] for Featvec in DataSet: if featvec[axis] = = value: #将划分依据从集合中删掉 Reducedfeatvec = Featvec[:axis] reducedfeatvec.extend (featvec[axis+1:]) Retdataset.append (Reducedfeatvec) return Retdataset Choose the best way to partition your data sets def choosebestfeaturetosplit (DataSet): numfeatures = Len (dataset[0])-1 baseentropy = calcshannonent (DataSet ) Bestinfogain = 0.0

First, IntroductionThe K-Nearest neighbor algorithm mentioned earlier is the simplest and most efficient algorithm for classifying data. The K-Nearest neighbor algorithm is an instance-based learning, and we must have training sample data close to the actual data when using the algorithm. Moreover, K-neighbor data must preserve all data sets, and if the training data set is large, a large amount of storage space must be used, and the K-nearest neighbor algorithm must calculate the distance for e

def load_data (): X1_tra in = Np.linspace (0,50,500) X2_train = Np.linspace ( -10,10,500) data_train = Np.array ([[X1,x2,f (X1,X2) + ( Np.random.random (1) -0.5)] x1,x2 in Zip (X1_train, X2_train)]) x1_test = Np.linspace (0,50,100) + 0.5 * Np.random.random (+) x2_test = Np.linspace ( -10,10,100) + 0.02 * Np.random.random (+) data_test = Np.array ([[ X1,x2,f (X1,X2)] for x1,x2 in Zip (X1_test, x2_test)]) return data_train, Data_test The image of the training set (th

: The last category is two men and one female, then judged as male:Classcount={} forVoteinchclasslist:ifVote not inchClasscount.keys (): Classcount[vote]=0 Classcount[vote]+=1Sortedclasscount= Sorted (Classcount.items (), Key=operator.itemgetter (1), reverse=True)returnSortedclasscount[0][0]defCreatetree (dataset,labels): Classlist=[EXAMPLE[-1] forExampleinchDataSet]#Category: Men and women ifClasslist.count (classlist[0]) = =Len (classlist):re

As the name implies, decision trees are based on tree structure to make decisions, which is a natural processing mechanism for human beings to face decision-making problems. For example, we're right, "is this a good melon?" The question is usually made in a series of judgments, first see what color it is, if it is gree

J48 principle: Originally named as C4.8, because it is the Java implementation of the algorithm, plus C4.8 for the commercial charge algorithm. In fact, J48 is a top-down, recursive division of the strategy, select a property placed in the root node, for each possible attribute value to produce a branch, the instance into multiple subsets, each subset corresponding to a branch of the root node, and then recursively repeat the process on each branch. When all instances have the same classificatio

General Decision tree Induction Framework See previous posts: http://blog.csdn.net/zhyoulun/article/details/41978381 ID3 Attribute Selection Metric principle ID3 uses information gain as a property selection metric. The measure is based on Shannon's pioneering work in the study of the value of messages or the information theory of "informative content". The node n represents or holds the tuple of partition

(), key=operator.itemgetter (1), reverse=true) returnsortedClassCount[0][0] #创建树, using recursive Defcreatetree (dataset,labels): #计算dataset里是否只有单值 classlist=[example[-1 ]forexampleindataset] #如果只有单值, and the only , a single value of ifclasslist.count (Classlist[0]) ==len (classList): is returned returnclasslist[0]# If it is the final result, but has multiple values, take the most iflen (Dataset[0]) ==1: NBSP;NBSP;NBSP;NBSP;RETURNNBSP;MAJORITYCNT (

): featlist = [Example[i] For example in DataSet] #建立特征i的链表 uniquevals = set (featlist) #set不允许重复 newentropy =0.0 F or value in Uniquevals:subdataset = Splitdataset (dataset,i,value) prob = Len (subdataset)/float (Len ( DataSet)) #计算满足特征i为value的概率 newentropy + = prob*calcshannonent (subdataset) Infogain = baseentropy-newent ropy if (Infogain > Bestinfogain): bestinfogain = Infogain bestfeature = i return BE

Decision Tree: ID3 algorithm: 1, Shannon entropy: If the transaction to be sorted may be divided into multiple classifications, then the information for x i is defined as:, where P (x i) chooses the probability of the classification. Entropy is defined as the expected value of information, and the calculation formula is: When the entropy is higher, the more data of different types, the higher the disorder o