28 lines
1.2 KiB
Markdown
28 lines
1.2 KiB
Markdown
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# Development
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The class structure and details algorithmic overview is depicted in the following diagram:
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As shown, there are three distinct objects comprising `FreqAI`:
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* IFreqaiModel
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* Singular persistent object containing all the necessary logic to collect data, store data, process data, engineer features, run training, and inference models.
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* FreqaiDataKitchen
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* A non-persistent object which is created uniquely for each unique asset/model. Beyond metadata, it also contains a variety of data processing tools.
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* FreqaiDataDrawer
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* Singular persistent object containing all the historical predictions, models, and save/load methods.
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There are a variety of built-in prediction models which inherit directly from `IFreqaiModel` including:
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* CatboostRegressor
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* CatboostRegressorMultiTarget
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* CatboostClassifier
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* LightGBMRegressor
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* LightGBMRegressorMultiTarget
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* LightGBMClassifier
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* XGBoostRegressor
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* XGBoostRegressorMultiTarget
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* XGBoostClassifier
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Each of these have full access to all methods in `IFreqaiModel`. And can therefore, override any of those functions at will. However, advanced users will likely stick to overriding `fit()`, `train()`, `predict()`, and `data_cleaning_train/predict()`.
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