vswift
Tools created for machine learning classification model evaluation (by donishadsmith)
Empirical_Study_of_Ensemble_Learning_Methods
Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning (by timothygmitchell)
vswift | Empirical_Study_of_Ensemble_Learning_Methods | |
---|---|---|
1 | 1 | |
1 | 10 | |
- | - | |
8.9 | 0.0 | |
about 1 month ago | over 3 years ago | |
R | R | |
MIT License | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
vswift
Posts with mentions or reviews of vswift.
We have used some of these posts to build our list of alternatives
and similar projects.
-
Seeking Feedback on my R Package for Categorical Model Validation
Here is the repo if anyone is interested: https://github.com/donishadsmith/vswift
Empirical_Study_of_Ensemble_Learning_Methods
Posts with mentions or reviews of Empirical_Study_of_Ensemble_Learning_Methods.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-01-04.
-
[P] Which Machine Learning Classifiers are best for small datasets? An empirical study
I've actually made the same kind of graph before. In this image: each point is the average of 5 out-of-fold predictions for one trial of k-fold cross-validation. I repeated the procedure 40 times to visualize the out-of-fold accuracy on the Wisconsin diagnostic breast cancer data set (560 observations on 30 numeric variables). I evaluated 14 models for classification:
What are some alternatives?
When comparing vswift and Empirical_Study_of_Ensemble_Learning_Methods you can also consider the following projects:
mlr3learners - Recommended learners for mlr3
optuna - A hyperparameter optimization framework
mlr3 - mlr3: Machine Learning in R - next generation
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
textfeatures - 👷♂️ A simple package for extracting useful features from character objects 👷♀️
psych-verbs - Research experiment design and classification of Romanian emotion verbs
tweetbotornot2 - 🔍🐦🤖 Detect Twitter Bots!
voice-gender - Gender recognition by voice and speech analysis
machine_learning_basics - Plain python implementations of basic machine learning algorithms
100-Days-Of-ML-Code - 100 Days of ML Coding
vswift vs mlr3learners
Empirical_Study_of_Ensemble_Learning_Methods vs optuna
vswift vs mlr3
Empirical_Study_of_Ensemble_Learning_Methods vs pyGAM
vswift vs textfeatures
Empirical_Study_of_Ensemble_Learning_Methods vs psych-verbs
vswift vs tweetbotornot2
Empirical_Study_of_Ensemble_Learning_Methods vs voice-gender
vswift vs machine_learning_basics
Empirical_Study_of_Ensemble_Learning_Methods vs 100-Days-Of-ML-Code