Conditional Probability Tree Estimation Analysis and Algorithms
نویسندگان
چکیده
We consider the problem of estimating the conditional probability of a label in time O(log n), where n is the number of possible labels. We analyze a natural reduction of this problem to a set of binary regression problems organized in a tree structure, proving a regret bound that scales with the depth of the tree. Motivated by this analysis, we propose the first online algorithm which provably constructs a logarithmic depth tree on the set of labels to solve this problem. We test the algorithm empirically, showing that it works succesfully on a dataset with roughly 10 labels.
منابع مشابه
Evaluation of estimation methods for parameters of the probability functions in tree diameter distribution modeling
One of the most commonly used statistical models for characterizing the variations of tree diameter at breast height is Weibull distribution. The usual approach for estimating parameters of a statistical model is the maximum likelihood estimation (likelihood method). Usually, this works based on iterative algorithms such as Newton-Raphson. However, the efficiency of the likelihood method is not...
متن کاملA Comparative Analysis of Methods for Probability Estimation Tree
In this paper, we address the problem of probability estimation of decision trees. This problem has received considerable attention in the areas of machine learning and data mining, and techniques to use tree models as probability estimators have been suggested. We make a comparative study of six well-known class probability estimation methods, measured by classification accuracy, AUC and Condi...
متن کاملLearning Näıve Bayes Tree for Conditional Probability Estimation
Näıve Bayes Tree uses decision tree as the general structure and deploys näıve Bayesian classifiers at leaves. The intuition is that näıve Bayesian classifiers work better than decision trees when the sample data set is small. Therefore, after several attribute splits when constructing a decision tree, it is better to use näıve Bayesian classifiers at the leaves than to continue splitting the a...
متن کاملConditional Independence Trees
It has been observed that traditional decision trees produce poor probability estimates. In many applications, however, a probability estimation tree (PET) with accurate probability estimates is desirable. Some researchers ascribe the poor probability estimates of decision trees to the decision tree learning algorithms. To our observation, however, the representation also plays an important rol...
متن کاملBayes Networks and Fault Tree Analysis Application in Reliability Estimation (Case Study: Automatic Water Sprinkler System)
In this study, the application of Bayes networks and fault tree analysis in reliability estimation have been investigated. Fault tree analysis is one of the most widely used methods for estimating reliability. In recent years, a method called "Bayes Network" has been used, which is a dynamic method, and information about the probable failure of the system components will be updated according to...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2009