Comments (4)
Refactored with base API. Further refactoring probably needed to easily integrate other ensemble classes.
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Need to consider how memory is shared among jobs.
Important to avoid serializing numpy arrays all across the place. How to do this best?
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Need also to consider that currently, preprocessing, fitting folds and fitting final estimators all call a new instance of Parallel. This is highly inefficient, since we would much prefer to share the pool of workers. But this means moving the Parallel call much higher up the hierarchy and thus significantly alters the backend infrastructure.
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Done.
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Related Issues (20)
- Serialize mlens superlearner with KerasRegressor inside HOT 1
- mlen superlearner for MIMO multi-input multi-output HOT 2
- Error when using sklearn StratifiedKFold in Evaluator CV HOT 1
- getting zero score accuracy on test data
- If I already have trained models, how can I use mlens HOT 3
- confirmation
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- Adding custom models in the superlearner
- Apply preprocessing to target variable as well
- Monotonic constraints
- Error when using preprocessing per case in model selection HOT 2
- Error involving Collections Module
- Getting error when executing the ensemble.fit(X_train, y_train) command HOT 1
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- Error while running ensemble.fit(X_train, y_train)
- Error in index/base.py when using NumPy 1.24 or higher - Replace `np.int` with `np.int_`
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