As mentioned in the title I'm using 300 Dataset example with 500 feature as an input.
As I'm training the dataset, I found something peculiar. Please look at the data shown below.
Iteration 5000 | Cost: 2.084241e-01
Training Set Accuracy: 100.000000
CV Set Accuracy: 85.000000
Test Set Accuracy: 97.500000
Iteration 3000 | Cost: 2.084241e-01
Training Set Accuracy: 98.958333
CV Set Accuracy: 85.000000
Test Set Accuracy: 97.500000
Iteration 1000 | Cost: 4.017322e-01
Training Set Accuracy: 96.875000
CV Set Accuracy: 85.000000
Test Set Accuracy: 97.500000
Iteration 500 | Cost: 5.515852e-01
Training Set Accuracy: 95.486111
CV Set Accuracy: 90.000000
Test Set Accuracy: 97.500000
Iteration 100 | Cost: 8.413299e-01
Training Set Accuracy: 90.625000
CV Set Accuracy: 95.000000
Test Set Accuracy: 97.500000
Iteration 50 | Cost: 8.483802e-01
Training Set Accuracy: 90.277778
CV Set Accuracy: 95.000000
Test Set Accuracy: 97.500000
The trend is that as the Iteration(cost) increases(cost decreases), the training set accuracy increases as expected, but the CV Set/Test Set Accuracy decreases. My initial thought is that this has to do with precision/bias issue, but I really can't buy it.
Anyone know what this entails? Or any reference?