EpiNow2
Empirical_Study_of_Ensemble_Learning_Methods
| EpiNow2 | Empirical_Study_of_Ensemble_Learning_Methods | |
|---|---|---|
| 1 | 1 | |
| 104 | 10 | |
| 1.9% | - | |
| 9.5 | 0.0 | |
| 4 days ago | over 3 years ago | |
| R | R | |
| GNU General Public License v3.0 or later | - |
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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.
EpiNow2
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Reproduction numbers for the third wave
Someone asked for reproduction numbers for Nova Scotia right now so I thought I'd post some generated with the EpiNow2 package. Some explanation is probably required.
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:
What are some alternatives?
GreenPass-Experiments - It's possible to create a valid and fake green pass? The scope of this project is try to create one.
optuna - A hyperparameter optimization framework
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
psych-verbs - Research experiment design and classification of Romanian emotion verbs
vswift - Tools created for machine learning classification model evaluation
voice-gender - Gender recognition by voice and speech analysis
100-Days-Of-ML-Code - 100 Days of ML Coding
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