nishiwen1214/PSForest
Paper of ACML 2020: "PSForest: Improving Deep Forest via Feature Pooling and Error Screening"
This project offers an improved machine learning model for classification and regression tasks using tabular data. It takes your structured datasets as input and provides more accurate predictions or classifications than traditional methods. Data scientists and machine learning engineers will find this useful for improving model performance.
No commits in the last 6 months.
Use this if you are building machine learning models and need to achieve higher accuracy for classification or regression problems with tabular data.
Not ideal if your primary data type is unstructured, like images, audio, or free-form text, or if you need to build deep learning models with neural networks.
Stars
41
Forks
2
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Sep 09, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/nishiwen1214/PSForest"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
grf-labs/grf
Generalized Random Forests
LAMDA-NJU/Deep-Forest
An Efficient, Scalable and Optimized Python Framework for Deep Forest (2021.2.1)
cerlymarco/linear-tree
A python library to build Model Trees with Linear Models at the leaves.
benedekrozemberczki/awesome-decision-tree-papers
A collection of research papers on decision, classification and regression trees with implementations.
ysraell/random-forest-mc
Random Forest with Dynamic Tree Selection Monte Carlo Based (RF-TSMC).