def crossval(clf=None, X=None, y=None, folds=10, n=5):
if X is None or y is None:
X, y = get_train_data()
clf = clf or get_model()
tot = 0
print("KFold folds={}, running {} times".format(folds, n))
for i in range(n):
res = cross_val_score(clf, X, y, cv=folds).mean()
tot += res
print("{}/{}: {}".format(i + 1, n, res))
print("-------- total --------")
print(tot / n)
return tot / n
def crossval(clf=None, X=None, y=None, folds=10, n=5, path=None):
if X is None or y is None:
X, y = get_train_data(path)
if len(X) < folds:
raise ValueError('There are not enough samples ({}). Need at least {}.'.format(len(X), folds))
clf = clf or get_model(path)
tot = 0
print("KFold folds={}, running {} times".format(folds, n))
for i in range(n):
res = cross_val_score(clf, X, y, cv=folds).mean()
tot += res
print("{}/{}: {}".format(i + 1, n, res))
print("-------- total --------")
print(tot / n)
return tot / n