I use the h2o deep learning using python on a data of 2 balanced classes "0" and "1", and adjusted the parameters to be as follows:
prostate_dl = H2ODeepLearningEstimator(
activation=,"Tanh"
hidden=[50,50,50],
distribution="multinomial",
score_interval=10,
epochs=1000,
input_dropout_ratio=0.2
,adaptive_rate=True
, rho=0.998, epsilon = 1e-8
)
prostate_dl .train(
x=x,
y=y,
training_frame =train,
validation_frame = test)
Each time the program runs gives different confusion matric and accuarcy results, can anyway explain that? how can the results can be reliable?
Also, all of the runs gives the majority prediction as class "1" not "0" , is their any suggestion?