Hi, Sometimes when using GridSearchCV, I realize that in the grid there are certain combinations of hyperparameters that are either incompatible or redundant. For example, when using an MLP, if I specify the following grid: grid = {'solver': ['sgd', 'adam'], 'learning_rate': ['constant', 'invscaling', 'adaptive']} then it yields the following ParameterGrid: [{'learning_rate': 'constant', 'solver': 'sgd'}, {'learning_rate': 'constant', 'solver': 'adam'}, {'learning_rate': 'invscaling', 'solver': 'sgd'}, {'learning_rate': 'invscaling', 'solver': 'adam'}, {'learning_rate': 'adaptive', 'solver': 'sgd'}, {'learning_rate': 'adaptive', 'solver': 'adam'}] Now, three of these are redundant, since learning_rate is used only for the sgd solver. Ideally I'd like to specify these cases upfront, and for that I have a simple hack ( https://github.com/jaidevd/jarvis/blob/master/jarvis/cross_validation.py#L38). Using that yields a ParameterGrid as follows: [{'learning_rate': 'constant', 'solver': 'adam'}, {'learning_rate': 'invscaling', 'solver': 'adam'}, {'learning_rate': 'adaptive', 'solver': 'adam'}] which is then simply removed from the original ParameterGrid. I wonder if there's a simpler way of doing this. Would it help if we had an additional parameter (something like "grid_exceptions") in GridSearchCV, which would remove these dicts from the list of parameters? Thanks