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DrXaos t1_ismok0x wrote

Reply to comment by pornthrowaway42069l in Testing Accuracy higher than Training Accuracy by redditnit21

In this case the train and test probably wasn't split stratified by classes, and there's an imbalance in the relative proportions of classes and there is some bias in the predictor.

And it's probably measuring top 1 accuracy which isn't the loss function being optimized directly.

  1. do a stratified test/train split
  2. measure more statistics on train vs test
  3. check dropout or other regularization differences
2

redditnit21 OP t1_isn2joq wrote

I am using a stratified test/train split. "train_df, test_df = model_selection.train_test_split(
df, test_size=0.2, random_state=42, stratify=df['Class']
)"

All the classes are equally proportioned except 1 class. I am using dropout layer in the model for training. Is the dropout layer creating this issue?

1

DrXaos t1_isn3k9e wrote

Certainly could be dropout. Dropout is on during training, stochastically perturbing activations in its usual form in packages, and off during test.

Take out dropout, use other regularization and report directly on your optimized loss function, train and test, often NLL if you're using a conventional softmax + CE loss function which is the most common for multinomial outcomes.

3

redditnit21 OP t1_isn465e wrote

>Views

Yeah I am using conventional softmax + CE loss function which is the most common for multinomial outcomes. Which regularization method would you suggest me to use and what's the main reason why test acc should be less than train acc?

1

DrXaos t1_isn4fs5 wrote

top 1 accuracy is a noisy measurement particularly if it's a binary 0/1 measurement.

A continuous performance statistic will more likely show the expected behavior of train perf better than test. Note on loss functions lower is better.

There's lots of regularization possible, but start with L2, weight decay, and/or limiting the size of your network.

1