decision-tree

问题 I'm working on a project that requires to run a ctree and then plot it in interactive mode - like the 'D3.js' tree layout, my main obstacle is to convert the ctree output into a json format, to later use by javascript. Following is what i need (with example from the iris data): > library(party) > irisct <- ctree(Species ~ .,data = iris) > irisct Conditional inference tree with 4 terminal nodes Response: Species Inputs: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width Number of

问题 I'm working on a project that requires to run a ctree and then plot it in interactive mode - like the 'D3.js' tree layout, my main obstacle is to convert the ctree output into a json format, to later use by javascript. Following is what i need (with example from the iris data): > library(party) > irisct <- ctree(Species ~ .,data = iris) > irisct Conditional inference tree with 4 terminal nodes Response: Species Inputs: Sepal.Length, Sepal.Width, Petal.Length, Petal.Width Number of

问题 I could manage to display total probabilities of my labels , for example after displaying my decision tree, I have a table : Total Predictions : 65% impressions 30% clicks 5% conversions But my issue is to find probabilities (or to count) by features (by node), for example : if feature1 > 5 if feature2 < 10 Predict Impressions samples : 30 Impressions else feature2 >= 10 Predict Clicks samples : 5 Clicks Scikit does it automatically , I am trying to find a way to do it with Spark 回答1: Note:

问题 I'm pretty new to R and I'm stuck with a pretty dumb problem. I'm calibrating a regression tree using the rpart package in order to do some classification and some forecasting. Thanks to R the calibration part is easy to do and easy to control. #the package rpart is needed library(rpart) # Loading of a big data file used for calibration my_data <- read.csv("my_file.csv", sep=",", header=TRUE) # Regression tree calibration tree <- rpart(Ratio ~ Attribute1 + Attribute2 + Attribute3 + Attribute4

问题 Confused about random_state parameter, not sure why decision tree training needs some randomness. My thoughts, (1) is it related to random forest? (2) is it related to split training testing data set? If so, why not use training testing split method directly (http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html)? http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html >>> from sklearn.datasets import load_iris >>>

问题 This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 7 years ago . I am using few data sets available online and trying to visualize tree. However, it does not let me visualize tree option at all. Could anyone please guide me how to get the tree diagram in weka by using data sets available online? 回答1: Look here http://maya.cs.depaul.edu/classes/ect584/weka/classify.html, for example. First you have to fit your decision tree (I used the J48

问题 I've been reading up on Decision Trees and Cross Validation, and I understand both concepts. However, I'm having trouble understanding Cross Validation as it pertains to Decision Trees. Essentially Cross Validation allows you to alternate between training and testing when your dataset is relatively small to maximize your error estimation. A very simple algorithm goes something like this: Decide on the number of folds you want (k) Subdivide your dataset into k folds Use k-1 folds for a

问题 I have an output from ctree() ( party package) that looks like the following. How do I get the list of splitting conditions for each terminal node, like like sns <= 0, dta <= 1; sns <= 0, dta > 1 and so on? 1) sns <= 0; criterion = 1, statistic = 14655.021 2) dta <= 1; criterion = 1, statistic = 3286.389 3)* weights = 153682 2) dta > 1 4)* weights = 289415 1) sns > 0 5) dta <= 2; criterion = 1, statistic = 1882.439 6)* weights = 245457 5) dta > 2 7) dta <= 6; criterion = 1, statistic = 1170

问题 In sci-kit learn, it's possible to access the entire tree structure, that is, each node of the tree. This allows to explore the attributes used at each split of the tree and which values are used for the test The binary tree structure has 5 nodes and has the following tree structure: node=0 test node: go to node 1 if X[:, 3] <= 0.800000011920929 else to node 2. node=1 leaf node. node=2 test node: go to node 3 if X[:, 2] <= 4.950000047683716 else to node 4. node=3 leaf node. node=4 leaf node.

问题 I've got a LabeledPoint on witch I want to run a decision tree (and later random forest) scala> transformedData.collect res8: Array[org.apache.spark.mllib.regression.LabeledPoint] = Array((0.0,(400036,[7744],[2.0])), (0.0,(400036,[7744,8608],[3.0,3.0])), (0.0,(400036,[7744],[2.0])), (0.0,(400036,[133,218,2162,7460,7744,9567],[1.0,1.0,2.0,1.0,42.0,21.0])), (0.0,(400036,[133,218,1589,2162,2784,2922,3274,6914,7008,7131,7460,8608,9437,9567,199999,200021,200035,200048,200051,200056,200058,200064