Download com.github.fracpete JAR files with all dependencies

HTTP for humans using Java, inspired by the amazing requests Python library.

Library for reading/writing jsonlines data using the gson library under the hood for automatic deserialization/serialization of objects.

Maximum Likelihood Rule Ensembles (MLRules) is a new rule induction algorithm for solving classification problems via probability estimation. The ensemble is built using boosting, by greedily minimizing the negative loglikelihood which results in estimating the class conditional probability distribution. The main advantage of decision rules is their simplicity and comprehensibility: they are logical statements of the form "if condition then decision", which is probably the easiest form of model to interpret. On the other hand, by exploiting a powerful statistical technique to induce the rules, the final ensemble has very high prediction accuracy. Fork of the original code located at: http://www.cs.put.poznan.pl/wkotlowski/software-mlrules.html

Classifier (regression/classification) using the LightGBM gradient boosting framework (https://github.com/microsoft/LightGBM).

Fork of the following defunct sourceforge.net project: https://sourceforge.net/projects/wekaclassalgos/

Binaries for Windows Intel 32bit.

Directory bookmarks for the JFileChooser Swing component.

Contains various algorithms for missing values imputation and injection.

Allows scripting in Java via JShell.

Contains various algorithms for missing values imputation and injection.