learning tree pmp

Special statement:　　The main post is the learning process of knowledge collation, in order to review later. Some of the content is from the network (if an excerpt is not indicated). If the content is wrong, please also correct me!Series Articles:1. Standard Trie dictionary Tree Learning One: Principle Analysis2. Standard Trie Dictionary

regenerate a decision tree with each decision tree, which describes the method of pickle serialization to store an already generated decision tree.The basic usage of the Pickle module is as follows.Use the Pickle module to store decision Trees:def storeTree(inputTree,filename): import pickle fw = open(filename,‘w‘) pickle.dump(inputTree,fw) fw.close()def grabTree(filename): import pickle

both original prefixes and arrays intN Cin>>N; presum[0]=0; for(inti =1; I i) {cin>>Nums[i]; Presum[i]= presum[i-1] +Nums[i]; } //for Presum sort note the length of the presum is n+1Sort (presum,presum+n+1); //to rejoin the uqsum.uqsum[cnt]= presum[0];//because presum[0] may be negative for(inti =1; I 1; ++i) {if(Presum[i]! =uqsum[cnt]) uqsum[++CNT] =Presum[i]; } CNT++;//CNT Representation number//Initialize the C arrayUpdate (FIND (0)+1,1);//if the prefix and no negative number: in

Tag:ima cannot implement series margin Performance learning divblog A tree array can be used to calculate the and of the interval elements. Unlike prefixes and practices, it supports modification of values. For example, now I have a sequence a that asks you to maintain the sequence so that it supports two operations. 1. Change the value of item K of the series 2. The gross violent practice of querying

and is no longer mentioned.5) Breadth-first traversalUse the characteristics of the queue to achieve. Implementation process: For any node p, if p is not empty, p is pressed into the queue. The first element of the team is assigned to P, and the corresponding value of the P node is output. If the left child node of P is not empty, the left child node is pressed into the queue, and if the right child node of P is not empty, the right child node is pressed into the queue. This loops until the ele

Decision trees are also supervised machine learning methods. In the movie "Shameless bastard" there is a game, in the German bistro there are several people playing 20 problem games, the rule of the game is that a fan pulls out a goal in a card (can be a person or a thing), and a riddle can ask a question, a fan can only answer yes or no, after a few questions (up to 20 questions), The riddles accurately found the answer by narrowing the scope. This i

Preface In the previous time has studied the NB naive Bayesian algorithm, and just a preliminary study of Bayesian network of some basic concepts and commonly used computational methods. So there is the first knowledge of Bayesian network article, because I have been studying learning naive Bayesian algorithm We have to start from the naïve Bayesian algorithm, because in the preface has been said that the tan algorithm is to enhance the NB algorithm,

left component weight, that has been to the right, it will be able to find the fastest node can be inserted elements. So the next definition: the left-leaning tree is to its arbitrary subtree, the distance to the right to the insertion point (hereinafter referred to as "distance") is always less than the left to the insertion point distance, of course, and the two fork heap, the parent node is less than the value of the child node. If the node itself

This article describes the python Machine Learning Decision tree in detail (demo-trees, DTs) is an unsupervised learning method for classification and regression. Advantages: low computing complexity, easy to understand output results, insensitive to missing median values, and the ability to process irrelevant feature dataDisadvantage: the problem of over-matchi

more complicated and we will find the Z successoron BST. As a result, this problem has become the successor problem we discussed earlier, in a total of two cases:The first case: Z's right child is the Z-successor we're looking for, so we'll just transplant the transplant with Z.right as the root subtree to Z.The second case: Z's right child is not the Z-successor we're looking for , so we'll start from Z.right, ' dig in ' and find the successor that knows Z, assuming Q. So we make Q the Father

to the root of the tree. There are three kinds of situations:(1) If the extra black node is red, the node is painted black to restore the red and black tree properties.(2) If the extra black node is black, and the node is the root, you do not need to adjust, still is red black tree.(3) If the extra black node is black, but the node is not root, there are four ca

I. OverviewThe decision tree is an important task for the knowledge information contained in the data, so the decision tree can use unfamiliar data collection, and extract a series of rules, when these machines based on data creation rules, is the machine learning process.Second, the structure of decision treeDecision Tree:Advantages: The computational complexity

XML documents form a tree structure. XML documents must contain root elements. This element is the parent element of all other elements. The elements in the XML document form a document tree. The tree starts from the root and expands to the bottom of the tree. All elements can have child elements: XML documents form a

minimizing the degree of impurity at each step, the cart can handle the outliers and be able to handle the vacancy values. The termination condition of the tree partition: 1, the node achieves the complete purity; 2, the depth of the tree reaches the depth of the user3, the number of samples in the node belongs to the user specified number;Pruning method of tree

The width of the binary treeThe width of the binary tree is defined as The total number of nodes across the binary tree, where the maximum value is the width of the binary tree. So the first layer of the binary tree is 1 (root node).Code implementation (c + +)Code implementation is relatively simple, the

The function of a tree array is the same as a line tree. But, this thing is really good to write @. @Learning Blog: tree-like arrayTree array The main words can achieve three functions ① single-point modification, interval query ② interval modification, single-point query. 3, interval modification, interval query. The

to take the derivative of S and to guide the value at SN pointThus, it looks as if H (x) is infinitely large; it is unscientific, so add a penalty for H (X).After penalize a toss, H finally has a smarty pants form: That is, regression with residuals.Next, we will solve the problem of moving amplitude .After some sex, Alphat also came out, is a single variable linear regression.After the groundwork has been done, succinctly gave the form of GBDT:1) Use Crt to learn {x, yn-sn}, keep this round of

AboutDTC =Decisiontreeclassifier () $ #Training - Dtc.fit (X_train, Y_train) - #Predicting saved results -Y_predict =dtc.predict (x_test) A + " " the 4 Model Evaluation - " " $ Print("accuracy:", Dtc.score (X_test, y_test)) the Print("Other indicators: \ n", Classification_report (Y_predict, Y_test, target_names=['died','survived'])) the " " the accuracy: 0.7811550151975684 the Other indicators: - Precision recall F1-score support in the died 0.91 0.78 0.84 236 the survived 0.58 0.80 0.67 Abo