Tree ensembles such as random forests and boosted trees are accurate but difficult to understand, debug and deploy. In this work, we provide the inTrees (interpretable trees) framework that extracts, measures, prunes and selects rules from a tree ensemble, and calculates frequent variable interactions. An rule-based learner, referred to as the simplified tree ensemble learner (STEL), can also b...

the classification and regression trees (cart) possess the advantage of being able to handlelarge data sets and yield readily interpretable models. in spite to these advantages, they are alsorecognized as highly unstable classifiers with respect to minor perturbations in the training data.in the other words methods present high variance. fuzzy logic brings in an improvement in theseaspects due ...

In machine learning ensemble methods have demonstrated high accuracy for the variety of problems in different areas. The most known algorithms intensively used in practice are random forests and gradient boosting. In this paper we present InfiniteBoost — a novel algorithm, which combines the best properties of these two approaches. The algorithm constructs the ensemble of trees for which two pr...

The aim of this work is to develop a tool to predict some pulp properties e.g., pulp yield, Kappa number, ISO brightness (ISO 2470:2008), fiber length and fiber width, using the sapwood and heartwood proportion in the raw-material. For this purpose, Acacia melanoxylon trees were collected from four sites in Portugal. Percentage of sapwood and heartwood, area and the stem eccentricity (in N-S an...

This research paper aims to evaluate the performance and accuracy of classification models based on decision trees(C5.0 & CART), Support Vector Machine(SVM) and Logistic Regression with a dataset. Three methods to detect fraud are presented. Automatic credit approval is the most significant process in the banking sector and financial institutions. It prevents the fraud which is going to happen....

Here, an independent statistical method is applied to validate the DILD network constructed by the ILP algorithm. To this end, the GUIDE algorithm is used [1, 2]. GUIDE is an algorithm that builds a classification and regression tree model to predict the values of one or more response variables (Y1, Y2, ...) from the values of the predictor variables (X1, X2, ...). It can also produce an import...

classification trees (j48) were induced to predict the habitat requirements of tench (tinca tinca). 306 datasets were used for the given fish during 8 years in the river basins in flanders (belgium). the input variables consisted of the structural-habitat (width, depth, gradient slope and distance from the source) and physic chemical (ph, dissolved oxygen, water temperature and electric conduct...

decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. the resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. the most comprehensible decision trees have been designed for perfect symbolic data. classical crisp decision trees (dt) are widely applied to classification t...