def train_svc():
# Selecting the model
return mp.ModelProperties(), svm.SVC(decision_function_shape='ovo')
def train_xgboost_classifier():
return mp.ModelProperties(), xgboost.XGBClassifier()
def train_random_forest():
# Selecting the model
return mp.ModelProperties(), RandomForestClassifier(
n_estimators=100) # Default estimators is 10
def train_knn():
# Selecting the model
return mp.ModelProperties(), neighbors.KNeighborsClassifier(
) # default is 5 neighbors
def train_sgd_regressor():
# Picking model
return mp.ModelProperties(regression=True,
online=True), linear_model.SGDRegressor()
def train_passive_aggressive_regressor():
# Picking model
return mp.ModelProperties(
regression=True,
online=True), linear_model.PassiveAggressiveRegressor()
def train_bayesian_ridge():
# Picking model
return mp.ModelProperties(regression=True), linear_model.BayesianRidge()
def train_xgboost_regressor():
return mp.ModelProperties(regression=True), xgboost.XGBRegressor()
def train_support_vector_regression():
# Picking model
return mp.ModelProperties(regression=True), svm.SVR()