decision-tree

I am trying to find how the C4.5 algorithm determines the threshold value for numeric attributes. I have researched and can not understand, in most places I've found this information: The training samples are first sorted on the values of the attribute Y being considered. There are only a finite number of these values, so let us denote them in sorted order as {v1,v2, …,vm}. Any threshold value lying between vi and vi+1 will have the same effect of dividing the cases into those whose value of the attribute Y lies in {v1, v2, …, vi} and those whose value is in {vi+1, vi+2, …, vm}. There are thus

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 = 10, cp = 0) fit_wine <- rpart(class ~ MalicAcid + Ash + AlcAsh + Mg + Phenols + Proa + Color + Hue + OD

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 >>> from sklearn.cross_validation import cross_val_score >>> from sklearn.tree import

I am using RandomForestClassifier implemented in python sklearn package to build a binary classification model. The below is the results of cross validations: Fold 1 : Train: 164 Test: 40 Train Accuracy: 0.914634146341 Test Accuracy: 0.55 Fold 2 : Train: 163 Test: 41 Train Accuracy: 0.871165644172 Test Accuracy: 0.707317073171 Fold 3 : Train: 163 Test: 41 Train Accuracy: 0.889570552147 Test Accuracy: 0.585365853659 Fold 4 : Train: 163 Test: 41 Train Accuracy: 0.871165644172 Test Accuracy: 0.756097560976 Fold 5 : Train: 163 Test: 41 Train Accuracy: 0.883435582822 Test Accuracy: 0.512195121951 I

Does anyone know how to calculate the error rate for a decision tree with R? I am using the rpart() function. Assuming you mean computing error rate on the sample used to fit the model, you can use printcp() . For example, using the on-line example, > library(rpart) > fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) > printcp(fit) Classification tree: rpart(formula = Kyphosis ~ Age + Number + Start, data = kyphosis) Variables actually used in tree construction: [1] Age Start Root node error: 17/81 = 0.20988 n= 81 CP nsplit rel error xerror xstd 1 0.176471 0 1.00000 1.00000 0.21559

I have been exploring scikit-learn, making decision trees with both entropy and gini splitting criteria, and exploring the differences. My question, is how can I "open the hood" and find out exactly which attributes the trees are splitting on at each level, along with their associated information values, so I can see where the two criterion make different choices? So far, I have explored the 9 methods outlined in the documentation. They don't appear to allow access to this information. But surely this information is accessible? I'm envisioning a list or dict that has entries for node and gain.

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 Note: the following solution is for Scala only. I didn't find a way to do it in Python. Assuming you just want a