GenomeDataScience/FastRandomForest
A fast implementation of the Random Forest algorithm for the Weka environment
This tool helps data scientists and researchers classify datasets more quickly within the Weka environment. You provide your structured data, often from genomics or other scientific fields, and it rapidly produces classifications and predictions. It's designed for those working with large datasets who need faster model training.
No commits in the last 6 months.
Use this if you are a data scientist or researcher working in Weka with large datasets, especially those with many instances or numeric/binary attributes, and need to speed up your Random Forest classification tasks.
Not ideal if your datasets contain many multi-categorical attributes (with 5 or more categories), are stored in a sparse format, or if you need to perform regression analysis.
Stars
8
Forks
1
Language
Java
License
—
Category
Last pushed
Sep 10, 2017
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/GenomeDataScience/FastRandomForest"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
google/yggdrasil-decision-forests
A library to train, evaluate, interpret, and productionize decision forest models such as Random...
parrt/dtreeviz
A python library for decision tree visualization and model interpretation.
tensorflow/decision-forests
A collection of state-of-the-art algorithms for the training, serving and interpretation of...
neurodata/treeple
Scikit-learn compatible decision trees beyond those offered in scikit-learn
winkjs/wink-regression-tree
Decision Tree to predict the value of a continuous target variable