The Galaxy Zoo Challenge asks participants to predict proportions of
user classifications of galaxy morphologies. Using the Extra-Trees
algorithm and data preprocessing and augmentation, an error rate of
0.12859 and rank of 145/329 was achieved. The key idea behind
the solution was to mimic the success of convolutional neural
networks by implementing a large model with lots of randomness to
combat overfitting. While this approach performed moderately well, a
feature engineering focused approach would likely perform better
given my computational limitations.

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