Working with Sklearn stratified kfold split, and when I attempt to split using multi-class, I received on error (see below). When I tried and split using binary, it works no problem.
num_classes = len(np.unique(y_train))
y_train_categorical = keras.utils.to_categorical(y_train, num_classes)
kf=StratifiedKFold(n_splits=5, shuffle=True, random_state=999)
# splitting data into different folds
for i, (train_index, val_index) in enumerate(kf.split(x_train, y_train_categorical)):
x_train_kf, x_val_kf = x_train[train_index], x_train[val_index]
y_train_kf, y_val_kf = y_train[train_index], y_train[val_index]
ValueError: Supported target types are: ('binary', 'multiclass'). Got 'multilabel-indicator' instead.
keras.utils.to_categorical
produces a one-hot encoded class vector, i.e. the multilabel-indicator
mentioned in the error message. StratifiedKFold
is not designed to work with such input; from the split
method docs:
split
(X, y, groups=None)[...]
y : array-like, shape (n_samples,)
The target variable for supervised learning problems. Stratification is done based on the y labels.
i.e. your y
must be a 1-D array of your class labels.
Essentially, what you have to do is simply to invert the order of the operations: split first (using your intial y_train
), and convert to_categorical
afterwards.