decision-tree

I saw the help in Matlab, but they have provided an example without explaining how to use the parameters in the 'classregtree' function. Any help to explain the use of 'classregtree' with its parameters will be appreciated. The documentation page of the function classregtree is self-explanatory... Lets go over some of the most common parameters of the classification tree model: x : data matrix, rows are instances, cols are predicting attributes y : column vector, class label for each instance categorical : specify which attributes are discrete type (as opposed to continuous) method : whether

I am trying to design a simple Decision Tree using scikit-learn in Python (I am using Anaconda's Ipython Notebook with Python 2.7.3 on Windows OS) and visualize it as follows: from pandas import read_csv, DataFrame from sklearn import tree from os import system data = read_csv('D:/training.csv') Y = data.Y X = data.ix[:,"X0":"X33"] dtree = tree.DecisionTreeClassifier(criterion = "entropy") dtree = dtree.fit(X, Y) dotfile = open("D:/dtree2.dot", 'w') dotfile = tree.export_graphviz(dtree, out_file = dotfile, feature_names = X.columns) dotfile.close() system("dot -Tpng D:.dot -o D:/dtree2.png")

I learn the machine learning with the scikit-learn library. I apply the decision tree classifier and the random forest classifier to my data with this code: def decision_tree(train_X, train_Y, test_X, test_Y): clf = tree.DecisionTreeClassifier() clf.fit(train_X, train_Y) return clf.score(test_X, test_Y) def random_forest(train_X, train_Y, test_X, test_Y): clf = RandomForestClassifier(n_estimators=1) clf = clf.fit(X, Y) return clf.score(test_X, test_Y) Why the result are so much better for the random forest classifier (for 100 runs, with randomly sampling 2/3 of data for the training and 1/3

I have built a decision tree model in R using rpart and ctree . I also have predicted a new dataset using the built model and got predicted probabilities and classes. However, I would like to extract the rule/path, in a single string, for every observation (in predicted dataset) has followed. Storing this data in tabular format, I can explain prediction with reason in a automated manner without opening R. Which means I want to got following. ObsID Probability PredictedClass PathFollowed 1 0.68 Safe CarAge < 10 & Country = Germany & Type = Compact & Price < 12822.5 2 0.76 Safe CarAge < 10 &

I've read from this documentation that : "Class balancing can be done by sampling an equal number of samples from each class, or preferably by normalizing the sum of the sample weights (sample_weight) for each class to the same value." But, it is still unclear to me how this works. If I set sample_weight with an array of only two possible values, 1 's and 2 's, does this mean that the samples with 2 's will get sampled twice as often as the samples with 1 's when doing the bagging? I cannot think of a practical example for this. Matt Hancock So I spent a little time looking at the sklearn