Empirical_Study_of_Ensemble_Learning_Methods
vswift
| Empirical_Study_of_Ensemble_Learning_Methods | vswift | |
|---|---|---|
| 1 | 1 | |
| 10 | 1 | |
| - | - | |
| 0.0 | 8.9 | |
| over 3 years ago | 29 days ago | |
| R | R | |
| - | MIT License |
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.
Empirical_Study_of_Ensemble_Learning_Methods
-
[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:
vswift
-
Seeking Feedback on my R Package for Categorical Model Validation
Here is the repo if anyone is interested: https://github.com/donishadsmith/vswift
What are some alternatives?
optuna - A hyperparameter optimization framework
mlr3learners - Recommended learners for mlr3
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
mlr3 - mlr3: Machine Learning in R - next generation
psych-verbs - Research experiment design and classification of Romanian emotion verbs
textfeatures - 👷♂️ A simple package for extracting useful features from character objects 👷♀️
voice-gender - Gender recognition by voice and speech analysis
tweetbotornot2 - 🔍🐦🤖 Detect Twitter Bots!
100-Days-Of-ML-Code - 100 Days of ML Coding
machine_learning_basics - Plain python implementations of basic machine learning algorithms
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
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.