jonathanwilton/PUExtraTrees
uPU, nnPU and PN learning with Extra Trees classifier.
This tool helps data scientists and machine learning practitioners build more accurate classification models when they have a dataset where only some positive examples are labeled, but all negative examples are unlabeled. It takes in your dataset with a mix of labeled positive, and unlabeled examples, and outputs a classification model that can predict positive and negative cases. It's designed for those working with incomplete labels who need robust model training.
No commits in the last 6 months.
Use this if you need to train a classification model but only have reliable labels for positive examples, with all other data points remaining unlabeled.
Not ideal if you have a dataset where all examples are clearly labeled as either positive or negative, or if you're not working with machine learning models.
Stars
19
Forks
1
Language
Python
License
MIT
Category
Last pushed
Dec 02, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/jonathanwilton/PUExtraTrees"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
stabgan/Multiple-Linear-Regression
Implementation of Multiple Linear Regression both in Python and R
SENATOROVAI/Normal-equation-solver-multiple-linear-regression-course
Multiple Linear Regression (MLR) models the linear relationship between a continuous dependent...
SENATOROVAI/Normal-equations-scalar-form-solver-simple-linear-regression-course
The normal equations for simple linear regression are a system of two linear equations used to...
SENATOROVAI/underfitting-overfitting-polynomial-regression-course
Underfitting and overfitting are critical concepts in machine learning, particularly when using...
andrescorrada/IntroductionToAlgebraicEvaluation
A collection of essays and code on algebraic methods to evaluate noisy judges on unlabeled test data.