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PREDICTING DRUG AND ALCOHOLABUSE TREATMENT COMPLETION BY LOOKING AT GUN VIOLENCE
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 ), classifiers = [KNeighborsClassifier(n_neighbors= 1 ), KNeighborsClassifier(n_neighbors= 3 ), KNeighborsClassifier(n_neighbors= 5 ), Only a few of the results are shown below. k-Nearest Neighbors, k = 1 || k-Fold, k = 25 Average Accuracy: 54.355% k-Nearest Neighbors, k =3 || Leave-One-Out Average Accuracy: 53.390% Bernoulli Naive Bayes || Shuffle Split Average Accuracy: 49.152% SVM, linear || Shuffle Split Average Accuracy: 51.525%
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