[Solved] Machine Learning Two class classification [closed]

Question

Outputs of a neural network are not probabilities (generally), so that could be a reason that you’re not getting the “1 – P” result you’re looking for.

Now, if it’s simple logistic regression, you’d get probabilities as output, but I’m assuming what you said is true and you’re using a super-simple neural network.

Also, what you may be changing is the bias “feature”, which could also lead to the model giving you the same result after training. Honestly there’s too little information in this post to say for certain what’s going on. I’d advise you try normalizing your features and trying again.

EDIT: Do you know if your neural network actually has 2 output nodes, or if it’s just one output node? If there are two, then the raw output doesn’t matter quite as much as which node had the higher output. If it’s just one, I’d look into thresholding it somewhere (like >0.5 means the price will rise, but <=0.5 means the price will fall, or however you want to threshold it.) Some systems used in applications where false positives are more acceptable than false negatives threshold at much lower values, like 0.2.

Accepted Answer

Outputs of a neural network are not probabilities (generally), so that could be a reason that you’re not getting the “1 – P” result you’re looking for.

Now, if it’s simple logistic regression, you’d get probabilities as output, but I’m assuming what you said is true and you’re using a super-simple neural network.

Also, what you may be changing is the bias “feature”, which could also lead to the model giving you the same result after training. Honestly there’s too little information in this post to say for certain what’s going on. I’d advise you try normalizing your features and trying again.

EDIT: Do you know if your neural network actually has 2 output nodes, or if it’s just one output node? If there are two, then the raw output doesn’t matter quite as much as which node had the higher output. If it’s just one, I’d look into thresholding it somewhere (like >0.5 means the price will rise, but <=0.5 means the price will fall, or however you want to threshold it.) Some systems used in applications where false positives are more acceptable than false negatives threshold at much lower values, like 0.2.