decision-tree | 易学教程

Rotate Classification Tree Terminal Barplot axis - R

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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)) 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 function: library(party) data(iris) attach(iris) ctree <- ctree(Species ~ Sepal.Length + Sepal.Width + Petal.Length

Map predictions back to IDs - Python Scikit Learn DecisionTreeClassifier

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I have a dataset that has a unique identifier and other features. It looks like this ID LenA TypeA LenB TypeB Diff Score Response 123-456 51 M 101 L 50 0.2 0 234-567 46 S 49 S 3 0.9 1 345-678 87 M 70 M 17 0.7 0 I split it up into training and test data. I am trying to classify test data into two classes from a classifier trained on training data. I want the identifier in the training and testing dataset so I can map the predictions back to the IDs . Is there a way that I can assign the identifier column as a ID or non-predictor like we can do in Azure ML Studio or SAS? I am using the

Incremental entropy computation

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Let std::vector<int> counts be a vector of positive integers and let N:=counts[0]+...+counts[counts.length()-1] be the the sum of vector components. Setting pi:=counts[i]/N , I compute the entropy using the classic formula H=p0*log2(p0)+...+pn*log2(pn) . The counts vector is changing --- counts are incremented --- and every 200 changes I recompute the entropy. After a quick google and stackoverflow search I couldn't find any method for incremental entropy computation. So the question: Is there an incremental method, like the ones for variance , for entropy computation? EDIT: Motivation for

how to get all terminal nodes - weight & response prediction 'ctree' in r

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问题 Here's what I can use to list weight for all terminal nodes : but how can I add some code to get response prediction as well as weight by each terminal node ID : say I want my output to look like this -- Here below is what I have so far to get the weight nodes(airct, unique(where(airct))) Thank you 回答1: The Binary tree is a big S4 object, so sometimes it is difficult to extract the data. But the plot method for BinaryTree object, hase an optional panel function of the form function(node)

Calculating prediction accuracy of a tree using rpart's predict method (R programming)

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问题 I have constructed a decision tree using rpart for a dataset. I have then divided the data into 2 parts - a training dataset and a test dataset. A tree has been constructed for the dataset using the training data. I want to calculate the accuracy of the predictions based on the model that was created. My code is shown below: library(rpart) #reading the data data = read.table("source") names(data) <- c("a", "b", "c", "d", "class") #generating test and train data - Data selected randomly with a

How to handle categorical features for Decision Tree, Random Forest in spark ml?

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问题 I am trying to build decision tree and random forest classifier on the UCI bank marketing data -> https://archive.ics.uci.edu/ml/datasets/bank+marketing. There are many categorical features (having string values) in the data set. In the spark ml document, it's mentioned that the categorical variables can be converted to numeric by indexing using either StringIndexer or VectorIndexer. I chose to use StringIndexer (vector index requires vector feature and vector assembler which convert features

How to set costs matrix for C5.0 Package in R?

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问题 I have googled much in the web, but don't find any useful description for the 'costs' parameter for C5.0 function in R. From the C5.0 R manual book, it just says "a matrix of costs associated with the possible errors. The matrix should have C columns and rows where C is the number of class levels". It does not tell me whether the row or the column is the predicated result by the model. Can anyone help? 回答1: Here is a quote from the help page of C5.0 (version 0.1.0-15): The cost matrix should

Extract and Visualize Model Trees from Sparklyr

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问题 Does anyone have any advice about how to convert the tree information from sparklyr's ml_decision_tree_classifier, ml_gbt_classifier, or ml_random_forest_classifier models into a.) a format that can be understood by other R tree-related libraries and (ultimately) b.) a visualization of the trees for non-technical consumption? This would include the ability to convert back to the actual feature names from the substituted string indexing values that are produced during the vector assembler. The

Sklearn : How to balance classification using DecisionTreeClassifier?

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问题 I have a data set where the classes are unbalanced. The classes are either 0 , 1 or 2 . How can I calculate the prediction error for each class and then re-balance weights accordingly in Sklearn . 回答1: If you want to fully balance (treat each class as equally important) you can simply pass class_weight='balanced' , as it is stated in the docs: The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples /

Plot decision tree in R (Caret)

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I have trained a dataset with rf method. For example: ctrl <- trainControl( method = "LGOCV", repeats = 3, savePred=TRUE, verboseIter = TRUE, preProcOptions = list(thresh = 0.95) ) preProcessInTrain<-c("center", "scale") metric_used<-"Accuracy" model <- train( Output ~ ., data = training, method = "rf", trControl = ctrl, metric=metric_used, tuneLength = 10, preProc = preProcessInTrain ) After thath, I want to plot the decission tree, but when I wirte plot(model) , I get this: plot(model) . If I write plot(model$finalModel) , I get this : plot(model$finalModel) I would like to plot the