GradientBoostingClassifier.fit accepts sparse X, but .predict does not #6101
Description
I have a sparse dataset that is too large for main memory if I call X.todense(). If I understand correctly, GradientBoostingClassifier.fit will accept my sparse X, but it is not currently possible to use GradientBoostingClassifier.predict on the results. It would be great if that were not the case.
Here is a minimal example of the issue:
from scipy import sparse
from sklearn.datasets.samples_generator import make_classification
from sklearn.ensemble import GradientBoostingClassifier
X, y = make_classification(n_samples=20, n_features=5, random_state=0)
X_sp = sparse.coo_matrix(X)
clf = GradientBoostingClassifier()
clf.fit(X,y)
clf.predict(X) # works
clf.fit(X_sp, y) # works
clf.predict(X_sp) # fails with TypeError: A sparse matrix was passed, but dense data is required.