Pass the estimator obtained after fitting to scoring function as a parameter
Suppose I want to do a RandomizedSearchCV with custom both estimator and scorer :
cross-validation
scikit-learn cross validation, negative values with mean squared error
When I use the following code with Data matrix X of size (952,144) and output vector y of size (952), mean_squared_error metric returns negative values, which is unexpected. Do you have any idea?
Using explicit (predefined) validation set for grid search with sklearn
I have a dataset, which has previously been split into 3 sets: train, validation and test. These sets have to be used as given in order to compare the performance across different algorithms.