Exemplo n.º 1
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def evaluate_smm(player_data, id):
    models = [LogisticRegression(penalty='l1'),
              MultinomialNB(),
              DecisionTreeClassifier(),
              SVC(probability=True, kernel='rbf', gamma=0.3),
              RandomForestClassifier(n_estimators=20, n_jobs=-1)]
    player_model = StackingModel(models)
    results = validate.cross_validate(player_data, player_model, 10)
    validate.save_roc_curves(results, "smm/" + str(id))
Exemplo n.º 2
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def evaluate_forest(player_data, id):
    player_model = RandomForestClassifier(n_estimators=20, n_jobs=-1)
    results = validate.cross_validate(player_data, player_model, 10)
    validate.save_roc_curves(results, "forest/" + str(id))
Exemplo n.º 3
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def evaluate_svm(player_data, id):
    player_model = SVC(probability=True, kernel='rbf', gamma=0.3)
    results = validate.cross_validate(player_data, player_model, 10)
    validate.save_roc_curves(results, "svm/" + str(id))
Exemplo n.º 4
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def evaluate_dtree(player_data, id):
    player_model = DecisionTreeClassifier()
    results = validate.cross_validate(player_data, player_model, 10)
    validate.save_roc_curves(results, "dtree/" + str(id))
Exemplo n.º 5
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def evaluate_naive(player_data, id):
    player_model = MultinomialNB()
    results = validate.cross_validate(player_data, player_model, 10)
    validate.save_roc_curves(results, "naive/" + str(id))
Exemplo n.º 6
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def evaluate_logit(player_data, id):
    player_model = LogisticRegression(penalty='l1')
    results = validate.cross_validate(player_data, player_model, 10)
    validate.save_roc_curves(results, "logit/" + str(id))