| commit | 4c97c1c376727f552a80c60217648c37d76fcb4e | [log] [tgz] |
|---|---|---|
| author | Matthias Boehm <mboehm7@gmail.com> | Fri Apr 14 20:17:04 2023 +0200 |
| committer | Matthias Boehm <mboehm7@gmail.com> | Fri Apr 14 20:17:04 2023 +0200 |
| tree | 76504779db109fc8846ae0bcf25fc03e2df3257d | |
| parent | 40f2f7a0536aca988a889bdebbef6f219a38aebe [diff] |
[SYSTEMDS-3149] Additional RSS impurity measure for regression trees This patch adds an additional impurity measure, beside gini and entropy, to the decisionTree and randomForest builtin functions. The new measure is rss (residual sum of squares) for regression in order to properly learn the tree with regard to the final accuracy metrics.
Overview: SystemDS is an open source ML system for the end-to-end data science lifecycle from data integration, cleaning, and feature engineering, over efficient, local and distributed ML model training, to deployment and serving. To this end, we aim to provide a stack of declarative languages with R-like syntax for (1) the different tasks of the data-science lifecycle, and (2) users with different expertise. These high-level scripts are compiled into hybrid execution plans of local, in-memory CPU and GPU operations, as well as distributed operations on Apache Spark. In contrast to existing systems - that either provide homogeneous tensors or 2D Datasets - and in order to serve the entire data science lifecycle, the underlying data model are DataTensors, i.e., tensors (multi-dimensional arrays) whose first dimension may have a heterogeneous and nested schema.
Quick Start Install, Quick Start and Hello World
Documentation: SystemDS Documentation
Python Documentation Python SystemDS Documentation
Issue Tracker Jira Dashboard
Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source
