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As a beginner in neural networks, I'm currently working on a project for predicting winning fighters in MMA matches. I understand that this is a complex task due to the nature of the sport.

Currently, I'm achieving an accuracy of 64.2% on my test dataset. My goal is to increase this accuracy to 70%.

The method I'm using involves a fully connected neural network with the following layers to achieve this score:

self.fc = nn.Sequential(
   nn.Linear(70, 90),
   nn.Linear(90, 110),
   nn.Linear(110, 70),
   nn.Linear(70, 50),
   nn.ReLU(),
   nn.Linear(50, 1),
   nn.Sigmoid()
) 

I'm only training for 5 epochs because I've noticed that if I train for more, the model starts overfitting.

One of my main challenges is the limited amount of data I have, which totals 7855 samples. I split this into 80% for training and 20% for testing.

It's worth mentioning that there are 70 inputs because each fighter has 35 statistics. Additionally, I'm uncertain whether it's better to have a single output where the network predicts the winning fighter, or two outputs where the network outputs the probabilities of each fighter winning.

I believe I'm facing a significant data scarcity issue.

I was wondering if there are techniques available for generating synthetic data (similar to using color filters or altering regions in image data) since I have such a small dataset.

I'm open to any ideas or advice!

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1 Answer 1

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You should consider putting RelU after every Linear layer (except the output layer). Chaining multiple linear layers without an activation like that is equivalent to a single linear layer, but you're restricting the rank of that layer.

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