When we want to add regressors, we have to adopt the folder structure so that we can factor out parts that are used by both regressors and classifiers. We're currently doing this in sktime-dl, see #22, however for sktime, it's a bit more complicated because we have additional methods for composition and model selection that is specific to tasks.
Here's my proposal:
├── forecasting
│ ├── base.py
│ ├── composition.py
│ ├── exponential_smoothing.py
│ ├── model_selection.py
│ └── ...
├── series_as_features # any better name? supervised?
│ ├── base.py # can be used for common base classes for classifiers and regressor
│ ├── classification
│ │ ├── dictionary_based.py
│ │ ├── frequency_based.py
│ │ └── ...
│ ├── clustering
│ │ └── kmeans.py
│ │ └── ...
│ ├── composition.py
│ ├── model_selection.py
│ └── regression
│ ├── dictionary_based.py
│ ├── frequency_based.py
│ └── ...
├── transformer
└── ...
Any thoughts?
When we want to add regressors, we have to adopt the folder structure so that we can factor out parts that are used by both regressors and classifiers. We're currently doing this in sktime-dl, see #22, however for sktime, it's a bit more complicated because we have additional methods for composition and model selection that is specific to tasks.
Here's my proposal:
Any thoughts?