def __init__(self, num_fourier_des=10): ml_alg_base.__init__(self) # self.reader = DatasetReader() self.num_fourier_des = num_fourier_des """ The following classifier configurations has been selected by the grid search These were the results of running the grid search for 5 times {'kernel': 'rbf', 'C': 100, 'degree': 2} 0.955 {'kernel': 'linear', 'C': 10, 'degree': 2} 0.97 {'kernel': 'linear', 'C': 10, 'degree': 2} 0.98 {'kernel': 'linear', 'C': 50, 'degree': 2} 0.97 {'kernel': 'linear', 'C': 10, 'degree': 2} 0.96 {'kernel': 'linear', 'C': 50, 'degree': 2} 0.98 {'kernel': 'linear', 'C': 10, 'degree': 2} 0.965 {'kernel': 'linear', 'C': 50, 'degree': 2} 0.97 {'kernel': 'linear', 'C': 50, 'degree': 2} 0.955 {'kernel': 'linear', 'C': 10, 'degree': 2} 0.97 """ self.learning_model = svm.SVC(C=10, kernel='linear')
def __init__(self, num_fourier_des=10): ml_alg_base.__init__(self) self.num_fourier_des = num_fourier_des """ The following classifier configurations has been selected by the grid search These were the results of running the grid search for 5 times {'penalty': 'l2', 'C': 10} 0.945 {'penalty': 'l1', 'C': 50} 0.965 {'penalty': 'l2', 'C': 50} 0.955 {'penalty': 'l2', 'C': 10} 0.935 {'penalty': 'l2', 'C': 50} 0.965 {'penalty': 'l2', 'C': 50} 0.94 {'penalty': 'l2', 'C': 100} 0.965 {'penalty': 'l2', 'C': 50} 0.97 {'penalty': 'l1', 'C': 10} 0.955 {'penalty': 'l1', 'C': 10} 0.97 """ self.learning_model = LogisticRegression(multi_class='multinomial', penalty="l2", C=10, solver="lbfgs")
def __init__(self, num_fourier_des = 10): ml_alg_base.__init__(self) # self.reader = DatasetReader() self.num_fourier_des = num_fourier_des """ The following classifier configurations has been selected by the grid search These were the results of running the grid search for 5 times {'kernel': 'rbf', 'C': 100, 'degree': 2} 0.955 {'kernel': 'linear', 'C': 10, 'degree': 2} 0.97 {'kernel': 'linear', 'C': 10, 'degree': 2} 0.98 {'kernel': 'linear', 'C': 50, 'degree': 2} 0.97 {'kernel': 'linear', 'C': 10, 'degree': 2} 0.96 {'kernel': 'linear', 'C': 50, 'degree': 2} 0.98 {'kernel': 'linear', 'C': 10, 'degree': 2} 0.965 {'kernel': 'linear', 'C': 50, 'degree': 2} 0.97 {'kernel': 'linear', 'C': 50, 'degree': 2} 0.955 {'kernel': 'linear', 'C': 10, 'degree': 2} 0.97 """ self.learning_model = svm.SVC(C=10, kernel='linear')
def __init__(self): ''' Constructor ''' ml_alg_base.__init__(self) self.dsr = DatasetReader() self.learning_model = naive_bayes.GaussianNB()
def __init__(self, num_fourier_des=10): ml_alg_base.__init__(self) self.num_fourier_des = num_fourier_des self.learning_model = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0, max_depth=5)
def __init__(self, num_fourier_des=10): ml_alg_base.__init__(self) self.num_fourier_des = num_fourier_des """ The following classifier configurations has been selected by the grid search These were the results of running the grid search for 5 times {'n_estimators': 100, 'criterion': 'gini', 'max_depth': 10} 0.945 {'n_estimators': 100, 'criterion': 'gini', 'max_depth': 25} 0.96 {'n_estimators': 100, 'criterion': 'entropy', 'max_depth': 20} 0.965 {'n_estimators': 80, 'criterion': 'entropy', 'max_depth': 25} 0.95 {'n_estimators': 100, 'criterion': 'gini', 'max_depth': 10} 0.945 """ self.learning_model = RandomForestClassifier(max_depth=10, n_estimators=100, criterion="gini")
def __init__(self, num_fourier_des = 10): ml_alg_base.__init__(self) self.num_fourier_des = num_fourier_des """ The following classifier configurations has been selected by the grid search These were the results of running the grid search for 5 times {'n_estimators': 100, 'criterion': 'gini', 'max_depth': 10} 0.945 {'n_estimators': 100, 'criterion': 'gini', 'max_depth': 25} 0.96 {'n_estimators': 100, 'criterion': 'entropy', 'max_depth': 20} 0.965 {'n_estimators': 80, 'criterion': 'entropy', 'max_depth': 25} 0.95 {'n_estimators': 100, 'criterion': 'gini', 'max_depth': 10} 0.945 """ self.learning_model = RandomForestClassifier(max_depth=10, n_estimators=100, criterion='gini')
def __init__(self, num_fourier_des = 10): ml_alg_base.__init__(self) self.num_fourier_des = num_fourier_des self.learning_model = GradientBoostingClassifier(n_estimators=100, learning_rate=1.0,max_depth=5)
def __init__(self, num_fourier_des = 10): ml_alg_base.__init__(self) self.num_fourier_des = num_fourier_des self.learning_model = AdaBoostClassifier(n_estimators=100)