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Igel 旨在零代码构建深度学习模型的模块

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

https://github.com/benhamner/Metrics

编辑