def gaussian_process(self, label, result_list): clf = GaussianProcessClassifier() return execute_predict_proba(clf, self.train_test_split, label, result_list, self.image_creator)
def mlp(self, label, result_list): clf = MLPClassifier() return execute_predict_proba(clf, self.train_test_split, label, result_list, self.image_creator)
def xgboost(self, label, result_list): clf = xgb.XGBClassifier(max_depth=10) return execute_predict_proba(clf, self.train_test_split, label, result_list, self.image_creator)
def gnb(self, label, result_list): clf = GaussianNB() return execute_predict_proba(clf, self.train_test_split, label, result_list, self.image_creator)
def random_forest(self, label, result_list): clf = ensemble.RandomForestClassifier() return execute_predict_proba(clf, self.train_test_split, label, result_list, self.image_creator)
def decision_tree(self, label, result_list): clf = tree.DecisionTreeClassifier() return execute_predict_proba(clf, self.train_test_split, label, result_list, self.image_creator)
def knn(self, label, result_list): clf = neighbors.KNeighborsClassifier(n_neighbors=3, weights='distance') return execute_predict_proba(clf, self.train_test_split, label, result_list, self.image_creator)