pyGAM
[HELP REQUESTED] Generalized Additive Models in Python (by dswah)
Empirical_Study_of_Ensemble_Learning_Methods
Training ensemble machine learning classifiers, with flexible templates for repeated cross-validation and parameter tuning (by timothygmitchell)
| pyGAM | Empirical_Study_of_Ensemble_Learning_Methods | |
|---|---|---|
| 2 | 1 | |
| 839 | 10 | |
| - | - | |
| 2.4 | 0.0 | |
| 21 days ago | over 3 years ago | |
| Python | R | |
| Apache License 2.0 | - |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.
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.
pyGAM
Posts with mentions or reviews of pyGAM.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2021-10-11.
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[P] glum: High performance Python generalized linear modeling, a glmnet alternative!
GLMs are nice and all, but I'd also like to see some love for GAMs. pygam looks pretty much dead for some years now.
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[P] Which Machine Learning Classifiers are best for small datasets? An empirical study
Ah they haven't quite gotten around to supporting multiclass classification yet! https://github.com/dswah/pyGAM/pull/213
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 pyGAM and Empirical_Study_of_Ensemble_Learning_Methods you can also consider the following projects:
scikit-learn - scikit-learn: machine learning in Python
optuna - A hyperparameter optimization framework
psych-verbs - Research experiment design and classification of Romanian emotion verbs
glum - High performance Python GLMs with all the features!
vswift - Tools created for machine learning model evaluation
DALEX - moDel Agnostic Language for Exploration and eXplanation
voice-gender - Gender recognition by voice and speech analysis
tabmat - Efficient matrix representations for working with tabular data
100-Days-Of-ML-Code - 100 Days of ML Coding
pyGAM vs scikit-learn
Empirical_Study_of_Ensemble_Learning_Methods vs optuna
pyGAM vs optuna
Empirical_Study_of_Ensemble_Learning_Methods vs psych-verbs
pyGAM vs glum
Empirical_Study_of_Ensemble_Learning_Methods vs vswift
pyGAM vs DALEX
Empirical_Study_of_Ensemble_Learning_Methods vs voice-gender
pyGAM vs tabmat
Empirical_Study_of_Ensemble_Learning_Methods vs 100-Days-Of-ML-Code