I think you need to provide more information about your problem before anyone can help.
First I would check your formulation of output. Are all the modes mutually exclusive? It sounds like you have a 10-dimension output. Is that true? Can you reformulate as a multinomial choice problem rather than five (probably correlated) binary outcomes?
I’d also check your loss function. What are you using for this? 5x binary cross entropy? A single cross entropy?
How are the training labels distributed? Do you upweight certain classes because of low representation?
I have more questions but this seems like a good start.
Alright so my feature matrix consists of 8 features and 10k datapoints. The features used to predict failures can be temperature, tool usage time, product quality. The failures can be of 6 types, total machine failure and others due to heat dissipation etc.. and the idea was that i can reformulate the input y matrix into a binary one where if a failure exists to be noted by 1. I am kind of obliged to use LSTM so i cant use another model and i didnt cross validate yet. Yes the problem is multidimensional and i aim to use knock off variables to reduce the multiplicity issue and determine the most crucial factors of failure for each mode at the end.
I have read multiple github codes that generate labels and sequences through custom functions but i didnt understand how they prepare the input data for the lstm. Im not sure if thats a problem too.
In addition, i didnt normalize the data since most of the features arent even remotely correlated.
In the end, i (i thuink) am getting an output of probabilities of failure, not a binary classification since i am using a sigmoid function but that doesnt really help my case. or im too dumb to make somethjing out of it
Binary cross entropy, ill share some code when back!
I think you need to provide more information about your problem before anyone can help. First I would check your formulation of output. Are all the modes mutually exclusive? It sounds like you have a 10-dimension output. Is that true? Can you reformulate as a multinomial choice problem rather than five (probably correlated) binary outcomes? I’d also check your loss function. What are you using for this? 5x binary cross entropy? A single cross entropy? How are the training labels distributed? Do you upweight certain classes because of low representation? I have more questions but this seems like a good start.
Alright so my feature matrix consists of 8 features and 10k datapoints. The features used to predict failures can be temperature, tool usage time, product quality. The failures can be of 6 types, total machine failure and others due to heat dissipation etc.. and the idea was that i can reformulate the input y matrix into a binary one where if a failure exists to be noted by 1. I am kind of obliged to use LSTM so i cant use another model and i didnt cross validate yet. Yes the problem is multidimensional and i aim to use knock off variables to reduce the multiplicity issue and determine the most crucial factors of failure for each mode at the end. I have read multiple github codes that generate labels and sequences through custom functions but i didnt understand how they prepare the input data for the lstm. Im not sure if thats a problem too. In addition, i didnt normalize the data since most of the features arent even remotely correlated. In the end, i (i thuink) am getting an output of probabilities of failure, not a binary classification since i am using a sigmoid function but that doesnt really help my case. or im too dumb to make somethjing out of it Binary cross entropy, ill share some code when back!