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This has happened to me, a complicated model couldn't solve the problem when a simpler one solved it in a few epochs. How is that? I believed that a more complicated model means more number of parameters and more number of parameters means a higher capability to solve a problem.

I have heard that people say, A simpler Model can perform well because it has a low variance. But, what does that even mean?

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  • $\begingroup$ What do you call "a simpler model" ? A model using less variables ? A model easier to implement ? With less parameters ? $\endgroup$ – BeamsAdept Oct 7 at 13:02
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High variance means that your model's performance varies from data to data, which is bad for the model.

For example:
You used a polynomial classifier with high degree, so it will overfit your training data resulting in good accuracy, but when tried on a different dataset it will yield less accuracy , resulting in variance.

This will help you understand

  • The image on the right is way too complex which results in overfitting.

  • The middle image shows a simpler model than the image on right, but fits the data better ie. generalize the data better

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