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I am trying to solve this Kaggle Problem using Neural Networks. I am using Pybrain Python Library. It's a classical supervised Learning Problem. In following code: 'data' variable is numpy array(892*8). 7 fields are my features and 1 field is my output value which can be '0' or '1'. from pybrain.datasets import ClassificationDataSet from pybrain.supervised.trainers import BackpropTrainer from pybrain.tools.shortcuts import buildNetwork dataset = ClassificationDataSet(7,1) for i in data: dataset.appendLinked(i[1:],i[0]) net = buildNetwork(7,9,7,1, bias = True,hiddenclass = SigmoidLayer, outclass = TanhLayer) trainer = BackpropTrainer(net, learningrate = 0.04, momentum = 0.96, weightdecay = 0.02, verbose = True) trainer.trainOnDataset(dataset, 8000) trainer.testOnData(verbose = True) After training my Neural Network, when I am testing it on Training Data, its always giving a single output for all inputs. Like: Testing on data: out: [ 0.075] correct: [ 1.000] error: 0.42767858 out: [ 0.075] correct: [ 0.000] error: 0.00283875 out: [ 0.075] correct: [ 1.000] error: 0.42744569 out: [ 0.077] correct: [ 1.000] error: 0.42616996 out: [ 0.076] correct: [ 0.000] error: 0.00291185 out: [ 0.076] correct: [ 1.000] error: 0.42664586 out: [ 0.075] correct: [ 1.000] error: 0.42800026 out: [ 0.076] correct: [ 1.000] error: 0.42719380 out: [ 0.076] correct: [ 0.000] error: 0.00286796 out: [ 0.076] correct: [ 0.000] error: 0.00286642 out: [ 0.076] correct: [ 1.000] error: 0.42696969 out: [ 0.076] correct: [ 0.000] error: 0.00292401 out: [ 0.074] correct: [ 0.000] error: 0.00274975 out: [ 0.076] correct: [ 0.000] error: 0.00286129 I have tried altering learningRate, weightDecay, momentum, number of hidden units, number of hidden layers, class of h

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