decision-tree

问题 I need to allow the users choose their own path by picking between two simple choices displayed on their screen in order to progress to the next set of choices, until they get to one of the endings, i.e something like this should be achieved: I have tried the following code, but only the left side is evaluated each time. I am wondering how can I achieve a results like the above image (covering all the branches)? For instance, if the user selects "No" the application shouldn't ask any further

问题 I'm doing a classification using rpart in R. The tree model is trained by: > tree <- rpart(activity ~ . , data=trainData) > pData1 <- predict(tree, testData, type="class") The accuracy for this tree model is: > sum(testData$activity==pData1)/length(pData1) [1] 0.8094276 I read a tutorial to prune the tree by cross validation: > ptree <- prune(tree,cp=tree$cptable[which.min(tree$cptable[,"xerror"]),"CP"]) > pData2 <- predict(ptree, testData, type="class") The accuracy rate for the pruned tree

问题 I am using decision tree in Weka and I have some continuous data, so when I use Weka it automatically find the threshold for me but for some reason I want to implement Decision Tree by myself so I need to know what approach to use to find the threshold to discretize my continuous data? 回答1: ID3 and C4.5 use entropy heuristic for discretization of continuous data. The method finds a binary cut for each variable (feature). You could apply the same method recursively to get multiple intervals

问题 I am using sk-learn python 27 and have output some decision tree feature results. Though I am not sure how to interpret the results. At first, I thought the features are listed from the most informative to least informative (from top to bottom), but examining the \nvalue it suggests otherwise. How do I identify the top 5 most informative features from the outputs or using python lines? from sklearn import tree tree.export_graphviz(classifierUsed2, feature_names=dv.get_feature_names(), out

问题 For a medical website I'm trying to model a questionnaire that should result in a range of possible diagnoses. The questionnaire is fairly complex with a lot of conditionals. I made a flowchart/decision tree to reflect this questionnaire. I'm using Django to make the website. Currently I'm thinking of using Python Graph to turn the flow chart into a weighted graph. Each question would be a node and each answer would be an edge+label. I could then walk through the complete graph and the

问题 As we can see from the sklearn document here, or from my experiment, all the tree structure of DecisionTreeClassifier is binary tree. Either the criterion is gini or entropy, each DecisionTreeClassifier node can only has 0 or 1 or 2 child node. But from the decision tree introduction slide(page 3), each node of theoretic decision tree can has more than 2 child node. So my question is why the decision tree structure is only binary tree (each DecisionTreeClassifier node can only has 1 or 2

问题 I am currently dealing with wine data in MMST package. I have split the whole dataset into training and test and build a tree like the following codes: library("rpart") library("gbm") library("randomForest") library("MMST") data(wine) aux <- c(1:178) train_indis <- sample(aux, 142, replace = FALSE) test_indis <- setdiff(aux, train_indis) train <- wine[train_indis,] test <- wine[test_indis,] #### divide the dataset into trainning and testing model.control <- rpart.control(minsplit = 5, xval =

问题 This is a follow-up question of my previous one: Better Class-Structure for logical expression parsing and evaluation Brief introduction: rules as strings combinations of logical-and , logical-or , logical-negation and grouping by parenthesis of identifiers (ID's) Example: "{100} AND (({101} OR {102}) OR ({103} AND {104})) AND NOT ({105} OR {106})" This gets currently evaluated into a binary-tree of nodes, that looks like this: Code taken from here: How to parse a boolean expression and load

问题 I have a classification tree analyzed using ctree() was wondering how can one rotate the terminal nodes so that the axes are vertical? library(party) data(iris) attach(iris) plot(ctree(Species ~ Sepal.Length + Sepel.Width + Petal.Length + Petal.Width, data = iris)) 回答1: Here is how I would go about it. Not the shortest answer, but I wanted to be as thorough as possible. Since we are plotting your tree, it's probably a good idea to look at the documentation for the appropriate plotting

问题 I have a classification tree analyzed using ctree() was wondering how can one rotate the terminal nodes so that the axes are vertical? library(party) data(iris) attach(iris) plot(ctree(Species ~ Sepal.Length + Sepel.Width + Petal.Length + Petal.Width, data = iris)) 回答1: Here is how I would go about it. Not the shortest answer, but I wanted to be as thorough as possible. Since we are plotting your tree, it's probably a good idea to look at the documentation for the appropriate plotting