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Models
Simple Start
To start off, I decided to try simple models that only looked at one score at a time. For these models, I simply labeled the targets as positive or negative, based on their sign. I then ran the following set of cross validators and classifiers:
cross_validators = [KFold(n_splits=K), # K = 25
ShuffleSplit(n_splits=5, test_size=.25, random_state=0),
LeaveOneOut()]
classifiers = [KNeighborsClassifier(n_neighbors=1),
KNeighborsClassifier(n_neighbors=3),
KNeighborsClassifier(n_neighbors=5),
BernoulliNB(),
SVC(kernel='linear')]
Some Results
Only a few of the results are shown below.
k-Nearest Neighbors, k = 1 || k-Fold, k = 25
Average Accuracy: 54.355%
Confusion Matrix:
k-Nearest Neighbors, k =3 || Leave-One-Out
Average Accuracy: 53.390%
Confusion Matrix:
Bernoulli Naive Bayes || Shuffle Split
Average Accuracy: 49.152%
Confusion Matrix:
SVM, linear || Shuffle Split
Average Accuracy: 51.525%
Confusion Matrix:
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