decision-tree

I'm building a binary classification tree using mutual information gain as the splitting function. But since the training data is skewed toward a few classes, it is advisable to weight each training example by the inverse class frequency. How do I weight the training data? When calculating the probabilities to estimate the entropy, do I take weighted averages? EDIT: I'd like an expression for entropy with the weights. State-value weighted entropy as a measure of investment risk. http://www56.homepage.villanova.edu/david.nawrocki/State%20Weighted%20Entropy%20Nawrocki%20Harding.pdf Robert Harvey

A lot univariate decision tree learner implementations (C4.5 etc) do exist, but does actually someone know multivariate decision tree learner algorithms? Bennett and Blue's A Support Vector Machine Approach to Decision Trees does multivariate splits by using embedded SVMs for each decision in the tree. Similarly, in Multicategory classification via discrete support vector machines (2009) , Orsenigo and Vercellis embed a multicategory variant of discrete support vector machines (DSVM) into the decision tree nodes. CART algorithm for decisions tree can be made into a Multivariate. CART is a

In the application that I'm working on right now, I need to periodically check eligibility of tens of thousands of objects for some kind of a service. The decision diagram itself is in the following form, just way larger: In each of the end nodes (circles), I need to run an action (change an object's field, log information etc). I tried using Drool Expert framework, but in that case I'd need to write a long rule for every path in the diagram leading to an end node. Drools Flow doesn't seem to be built for such a use case either - I take an object and then, depending on the decisions along the

A little background: as a way to learn multinode trees in C++, I decided to generate all possible TicTacToe boards and store them in a tree such that the branch beginning at a node are all boards that can follow from that node, and the children of a node are boards that follow in one move. After that, I thought it would be fun to write an AI to play TicTacToe using that tree as a decision tree. TTT is a solvable problem where a perfect player will never lose, so it seemed an easy AI to code for my first time trying an AI. Now when I first implemented the AI, I went back and added two fields to

I'm trying to visualize my DecisionTree, but getting the error The code is: X = [i[1:] for i in dataset]#attribute y = [i[0] for i in dataset] clf = tree.DecisionTreeClassifier() dot_data = StringIO() tree.export_graphviz(clf.fit(train_X, train_y), out_file=dot_data) graph = pydot.graph_from_dot_data(dot_data.getvalue()) graph.write_pdf("tree.pdf") And the error is Traceback (most recent call last): if data.startswith(codecs.BOM_UTF8): TypeError: startswith first arg must be str or a tuple of str, not bytes Can anyone explain me whats the problem? Thank you a lot! I had the same exact problem

I can't seem to find a pointer in the right direction, I am not even sure what the terms are that I should be researching but countless hours of googling seem to be spinning me in circles, so hopefully the collective hive of intelligence of Stack Overflow can help. The problem is this, I need a way to filter data in what I can only call a compound logic tree. Currently the system implements a simple AND filtering system. For example, lets say we have a dataset of people. You add a bunch of filters such that show all the people where (Sex = Female) AND (Age > 23) AND (Age < 30) AND ( Status =