questions.txt

1. The curve demonstrates a positive correlation. As the percentage of training
data used increases, the accuracy on the test set also increases. This is because
with more training data, the computer can more accurately determine what to do
with 'random' values in the test set.

2. The noise is more apparently when a lower percentage of the data was used for
training. This is probably because when there is a little training, the program
can probably 'narrow' down to the right values but will be forced to 'choose' within
a small subset, and thus sometimes get it right (so it is more accurate) and
sometimes get it wrong (less accurate).

3. The curve is smooth around 100 trials or so.

4. Varying C determines the percentage of necessary training data needed to have
a higher accuracy on the test data. A higher C means less training data needed for
the same accuracy, and a smaller C value means that it need more training data to
reach the same accuracy threshhold.