decision-tree

I am writing a decision tree and the following code is a part of the complete code: def show_tree(tree, features, path): f = io.StringIO() export_graphviz(tree, out_file=f, feature_names=features) pydotplus.graph_from_dot_data(f.getvalue()).write_png(path) img = misc.imread(path) plt.rcParams['figure.figsize'] = (20,20) plt.imshow(img) Could any one please tell me what is the purpose of using StringIO here? Python is not my leading language, however I think answer for your question is quite simple and does not require lot of research. StringIO is used here to maintain Input/Output text stream.

I created a dataframe: totalDeposit <- cumsum(testd$TermDepositAMT[s1$ix]) which is basically calculating cumulative sum of TermDeposit amounts in testd dataframe and storing it in total deposit. This works perfrectly ok. I then need to calculate the average of the deposit amount and i use the following: avgDeposit <- totalDeposit / (1:testd) but get an error message Error in 1:testd : NA/NaN argument In addition: Warning message: In 1:testd : numerical expression has 19 elements: only the first used testd has some 8000 observations and 19 variables. Could someone help me get past this issue?

While working with the DecisionTreeClassifier I visualized it using graphviz , and I have to say, to my astonishment, it seems it takes categorical data and uses it as continuous data. All my features are categorical and for example you can see the following tree (please note that the first feature, X[0], has 6 possible values 0, 1, 2, 3, 4, 5: From what I found here the class uses a tree class which is a binary tree, so it is a limitation in sklearn. Anyone knows a way that I am missing to use the tree categorically? (I know it is not better for the task but as I need categories currently I

I wanted the decision path (i.e the set of rules) from the root node to a given node (which I supply) in a decision tree (DecisionTreeClassifier) in scikit-learn. clf.decision_path specifies the nodes a sample goes through, which may help in getting the set of rules followed by the sample, but how do you get the set of rules up to a particular node in the tree? For the decision rules of the nodes using the iris dataset : from sklearn.datasets import load_iris from sklearn import tree import graphviz iris = load_iris() clf = tree.DecisionTreeClassifier() clf = clf.fit(iris.data, iris.target)

I have a data set with 6 categorical variables with levels ranging from 5 to 28. I have obtained an output from ctree() (party package) with 17 terminal nodes. I have followed the inputs by @Galled from ctree() - How to get the list of splitting conditions for each terminal node? to arrive at my desired output. But, I'm getting the following error post running the code: Error in data.frame(ResulTable, Means, Counts) : arguments imply differing number of rows: 17, 2 I have tried adding this extra lines: ResulTable <- rbind(ResulTable, cbind(Node = Node, Path = Path2)) ResulTable$Node <-

I am guessing that it is conditional probability given that the above (tree branch) condition exists. However, I am not clear on it. If you want to read more about the data used or how do we get this diagram then go to : http://machinelearningmastery.com/visualize-gradient-boosting-decision-trees-xgboost-python/ Attribute leaf is the predicted value. In other words, if the evaluation of a tree model ends at that terminal node (aka leaf node), then this is the value that is returned. In pseudocode (the left-most branch of your tree model): if(f1 < 127.5){ if(f7 < 28.5){ if(f5 < 45.4){ return 0

I have a dictionary object as such: menu = {'dinner':{'chicken':'good','beef':'average','vegetarian':{'tofu':'good','salad':{'caeser':'bad','italian':'average'}},'pork':'bad'}} I'm trying to create a graph (decision tree) using pydot with the 'menu' data this . 'Dinner' would be the top node and its values (chicken, beef, etc.) are below it. Referring to the link, the graph function takes two parameters; a source and a node. It would look something like this : Except 'king' would be 'dinner' and 'lord' would be 'chicken', 'beef', etc. My question is: How do I access a key in a value? To create