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))
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))
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))
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))
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))
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))