Over the past few years, open source decision tree software tools have been in high demand for solving analytics and predictive data mining problems.
Decision trees are a popular type of supervised learning algorithm that builds classification or regression models in the shape of a tree (that’s why they are also known as regression and classification trees). They work for both categorical data and continuous data.
On this page, we collected 10 best open source license classification tree software solutions that run on Windows, Linux, and Mac OS X.
This is a Java-based free and open source tool for Windows, Linux, and Mac OS X. Weka is a powerful collection of machine learning algorithms for data mining purposes.
The algorithms can be applied directly to a dataset as well as called from Java code. Weka contains tools for classification, regression, visualization and clustering, and association rules.
Moreover, Weka has free online courses that teach machine learning and data mining using Weka.
KNIME Analytics Platform is one of the best open solutions for data-driven innovation. The platform is fast to deploy, easy to scale, and very intuitive to learn.
KNIME will provide you with 1500 modules, hundreds of ready-to-run examples (including decision tree examples), a variety of integrated tools, and an extremely wide choice of advanced algorithms. Great software for any data scientist.
In addition, KNIME has great examples of how to build prediction or classification model using a decision tree algorithm.
Knime is available for Windows, Linux, and Mac OSX.
3. Rapid Miner
This is a very powerful and popular data mining software solution which provides you with predictive advanced analytics. And it is one of the best open source decision tree software tool with no-coding required.
Written in Java, it holds a variety of data mining functions such as visualization, data pre-processing, cleansing, filtering, clustering, and predictive analysis. Its Decision Tree operator generates a decision tree model, which can be used for classification and regression.
Also, it is easily integrated with WEKA.
Rapid Miners runs on Windows, Linux, and Mac OSX.
SilverDecisions is a free and open source decision tree software with a great set of layout options. It is a specialized software for creating and analyzing decision trees.
The decision tree can be easily exported to JSON, PNG or SVG format.
In addition, they will provide you with a rich set of examples of decision trees in different areas such as research and development project decision tree, city council management software and etc.
The software works on Windows, Linux, and Mac OS X.
Orange is a free and open source data visualization software and machine learning tool for novice and expert. Orange will surprise you!
It allows you to explore statistical distributions, box and whisker plots, scatter plots, or dive much deeper with decision trees, heatmaps, MDS, hierarchical clustering, and linear regression models.
In addition, Orange graphic user interface allows you to focus on exploratory data analysis instead of coding. Orange is used for teaching at schools, universities and in professional training courses all over the world.
When it comes to decision trees, Orange has numerous implementations of classification tree learners: TreeLearner, SimpleTreeLearner, and a C45Learner.
Orange works on Windows, Linux, and Mac OS X.
SMILES is an open source machine learning software system that incorporates different features from other machine learning techniques and paradigms and, more importantly, it presents several innovations in almost all of these features.
SMILES extends classical decision tree learners with new splitting criteria, new partitions, non-greedy search, extraction of several and different solutions.
Its applications are basically data-mining and any other machine learning task where decision trees could be useful. SMILES can also extract comprehensible models from ensembles of classifiers or from neural networks (through mimicking).
The software has been checked on several Intel-based machines (PCs) under different versions of Linux and Ms. Windows.
Scikit-learn is a simple but efficient machine learning library for Python and an open source tool for data mining and data analysis. It is accessible to everybody and reusable in various contexts.
It includes a wide variety of algorithms and methods, and decision tree classification is very well supported. Scikit-learn features tree algorithms: ID3, C4.5, C5.0, and CART.
Scikit-learn makes it easy to create classification tree that predicts the value of a target variable by learning simple decision rules inferred from the data features.
Runs on Windows, Mac, and Linux.
8. OC1 Decision Tree Software System
An open source decision tree software system designed for applications where the instances have continuous values (see discrete vs continuous data). The OC1 software allows the user to create both standard, axis-parallel decision trees and oblique (multivariate) trees.
OC1 has been used for classification of data representing diverse problem domains, including astronomy, gene finding, and others.
OC1 is written in ANSI C, and combines a variety of features designed to support flexible experimentation on a variety of types of data. It provides support for cross-validation experiments, generation of artificial data, and graphical display of data sets and decision trees.
9. Simple Decision Tree
This is an Excel Add-in released under the terms of GPL v3 with additional permissions. It is initially created for the Stanford Decisions and Ethics Center and open-sourced it for the Decision Professionals Network.
It is good if you need a really simple open source decision tree solution.
Rattle is a popular GUI for data mining using R. It is also a great solution when it comes to open source decision tree software. The tool runs on Linux, Mac OS, and Windows.
Rattle is a powerful tool that presents statistical and visual summaries of data, builds both unsupervised and supervised machine learning models from the data, presents the performance of models graphically and etc.
Except for business and commercial enterprises purposes, Rattle is used also for teaching in Australian and American universities.
In addition, Rattle has a number of decision tree examples you can use.