Empirical_Study_of_Ensemble_Learning_Methods
pyGAM
| Empirical_Study_of_Ensemble_Learning_Methods | pyGAM | |
|---|---|---|
| 1 | 2 | |
| 10 | 839 | |
| - | - | |
| 0.0 | 2.4 | |
| over 3 years ago | 26 days ago | |
| R | Python | |
| - | Apache License 2.0 |
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Empirical_Study_of_Ensemble_Learning_Methods
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[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:
pyGAM
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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
What are some alternatives?
optuna - A hyperparameter optimization framework
scikit-learn - scikit-learn: machine learning in Python
psych-verbs - Research experiment design and classification of Romanian emotion verbs
vswift - Tools created for machine learning classification model evaluation
glum - High performance Python GLMs with all the features!
voice-gender - Gender recognition by voice and speech analysis
DALEX - moDel Agnostic Language for Exploration and eXplanation
100-Days-Of-ML-Code - 100 Days of ML Coding
tabmat - Efficient matrix representations for working with tabular data
STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDA - Forecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning to Forecast Stock Return. Approach Used by Hedge Funds to Select Tradeable Stocks
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation