I would like to use K-fold cross-validation on my data of my model.
My codes in Keras is :
a = np.array( [[283, 95, 72, 65], [290, 100, 80, 72], [120,170,130,122], [100,230,110,200], [300,100,200,500]] ) X = a[:,0:2] Y = a[:,3] from sklearn.model_selection import KFold, cross_val_score k_fold = KFold(n_splits=3) model = models.Sequential() model.add(Dense(12, input_shape=(3,))) model.add(LeakyReLU()) model.summary() cross_val_score(model,X,Y)
But, It makes this error:
If no scoring is specified, the estimator passed should have a 'score' method. The estimator does not.
And when I select a scoring parameter as:
cross_val_score(model,X,Y, scoring= 'accuracy')
It makes another error:
TypeError: Cannot clone object '' (type ): it does not seem to be a scikit-learn estimator as it does not implement a 'get_params' methods.
How can I use K-fold cross-validation on this model?