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Many factors negatively affect a generalization of the findings in discovery proteomics. They include differentiation between patient cohorts, a variety of experimental conditions, etc. We presented a ...

The visual data mining process, seen in the first part of this two-part article, revealed patterns in four dimensions between cumulative gas well production and independent variables ...

The decision tree makes predictions based on this tendency toward a particular outcome. For continuous attributes, the algorithm uses linear regression to determine where a decision tree splits.

This project uses Weka to analyze the "Car Evaluation" dataset with decision trees, comparing model performance on 70/30 and 50/50 data splits. It includes accuracy, F1-scores, and decision tree ...

While Markov Chain Monte Carlo methods are typically used to construct Bayesian Decision Trees, here we provide a deterministic Bayesian Decision Tree algorithm that eliminates the sampling and does ...

As aforementioned, decision tree algorithms allow us to build a model of the problem based on a training data set, which is easily understandable and which also allows us to predict a future behavior.