Empirical_Study_of_Ensemble_Learning_Methods
optuna
| Empirical_Study_of_Ensemble_Learning_Methods | optuna | |
|---|---|---|
| 1 | 34 | |
| 10 | 9,714 | |
| - | 2.2% | |
| 0.0 | 9.9 | |
| over 3 years ago | about 5 hours ago | |
| R | Python | |
| - | GNU General Public License v3.0 or later |
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:
optuna
-
Optuna – A Hyperparameter Optimization Framework
I didn’t even know WandB did hyperparameter optimization, I figured it was a neural network visualizer based on 2 minute papers. Didn’t seem like many alternatives out there to Optuna with TPE + persistence in conditional continuous & discrete spaces.
Anyway, it’s doable to make a multi objective decide_to_prune function with Optuna, here’s an example https://github.com/optuna/optuna/issues/3450#issuecomment-19...
- How to test optimal parameters
- FOSS hyperparameter optimization framework to automate hyperparameter search
-
How did you make that?!
The network configuration process is usually not particularly scientific and mostly relies on empirical observation. For some cases, tools like Optuna can be used to automatically find the optimal parameters. In others, on others, you can look for modern studies which explore the effect of this parameter on performance, such as this study (2022), but these are typically very specific to one particular architecture.
-
[P] We are building a curated list of open source tooling for data-centric AI workflows, looking for contributions.
Keras Tuner, Optuna : https://github.com/optuna/optuna ?
- How to tune more than 2 hyperparameters in Grid Search in Python?
-
Suggestion to optimize algo
I have used OpenTuner, but I don't think it is maintained anymore. I hear tell that Optuna is what to use now, but have not used it myself. https://optuna.org Optuna - A hyperparameter optimization framework
-
Best practices for training PyTorch model
Research the type of model to get an idea of what hyper parameters to use. I recommend using a hyper parameter optimization library like Optuna to get the best configuration
-
[D]How to optimize an ANN?
You can use Optuna, SMAC or hyperopt
What are some alternatives?
pyGAM - [HELP REQUESTED] Generalized Additive Models in Python
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
psych-verbs - Research experiment design and classification of Romanian emotion verbs
hyperopt - Distributed Asynchronous Hyperparameter Optimization in Python
vswift - Tools created for machine learning classification model evaluation
rl-baselines3-zoo - A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.
voice-gender - Gender recognition by voice and speech analysis
nni - An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
100-Days-Of-ML-Code - 100 Days of ML Coding
mljar-supervised - Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
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