Igel 旨在零代码构建深度学习模型的模块
Published on Aug. 22, 2023, 12:07 p.m.
2021-06-14 记事
Igel 旨在零代码构建深度学习 模型的模块
a delightful machine learning tool that allows you to train, test, and use models without writing code
igel.readthedocs.io/en/latest/
用法很独特,直接docker
类似如下
docker run -it --rm -v $(pwd):/data nidhaloff/igel fit -yml 'your\_file.yaml' -dp 'your\_dataset.csv'
基本都是靠yaml配置文件搞定。
下面是它们支持的模型,分类回归和聚类,复杂点的就搞不定了。
Igel's supported models:
+--+-+-+
| regression | classification | clustering |
+--+-+-+
| LinearRegression | LogisticRegression | KMeans |
| Lasso | Ridge | AffinityPropagation |
| LassoLars | DecisionTree | Birch |
| BayesianRegression | ExtraTree | AgglomerativeClustering |
| HuberRegression | RandomForest | FeatureAgglomeration |
| Ridge | ExtraTrees | DBSCAN |
| PoissonRegression | SVM | MiniBatchKMeans |
| ARDRegression | LinearSVM | SpectralBiclustering |
| TweedieRegression | NuSVM | SpectralCoclustering |
| TheilSenRegression | NearestNeighbor | SpectralClustering |
| GammaRegression | NeuralNetwork | MeanShift |
| RANSACRegression | PassiveAgressiveClassifier | OPTICS |
| DecisionTree | Perceptron | - |
| ExtraTree | BernoulliRBM | - |
| RandomForest | BoltzmannMachine | - |
| ExtraTrees | CalibratedClassifier | - |
| SVM | Adaboost | - |
| LinearSVM | Bagging | - |
| NuSVM | GradientBoosting | - |
| NearestNeighbor | BernoulliNaiveBayes | - |
| NeuralNetwork | CategoricalNaiveBayes | - |
| ElasticNet | ComplementNaiveBayes | - |
| BernoulliRBM | GaussianNaiveBayes | - |
| BoltzmannMachine | MultinomialNaiveBayes | - |
| Adaboost | - | - |
| Bagging | - | - |
| GradientBoosting | - | - |
+--+-+-+
https://github.com/nidhaloff/igel
Forecasting #时间序列
Time Series Forecasting Best Practices & Examples https://github.com/microsoft/forecasting
自动深度学习资源总结
https://github.com/HuaizhengZhang/Awesome-System-for-Machine-Learning
https://github.com/D-X-Y/Awesome-AutoDL
Kaggler
Kaggler 是一个 Python 包,用于 ETL 和数据分析的轻量级在线机器学习算法和实用函数。它是在 MIT 许可证下分发的。
https://github.com/jeongyoonlee/Kaggler
Tabular Datasets !
https://github.com/carefree0910/carefree-learn
Metrics