def knnTuning():
train = getTrainingData('train.csv', visualize=False)
X = train.drop(['Exited'], axis=1)
sc = StandardScaler()
X = sc.fit_transform(X)
y = train.Exited
# split training data half half
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=0)
params = {
"n_neighbors": list(range(5, 131, 2)),
"weights": ['uniform', 'distance']
}
model = neighbors.KNeighborsClassifier()
grid_search_cv = GridSearchCV(model,
params,
verbose=1,
n_jobs=-1,
cv=3,
scoring='accuracy')
# print(grid_search_cv.best_params_)
grid_search_cv.fit(X_train, y_train)
print_score(grid_search_cv, X_train, y_train, X_test, y_test, train=True)
print_score(grid_search_cv, X_train, y_train, X_test, y_test, train=False)
ROC(grid_search_cv, X_train, y_train, X_test, y_test, train=True)
ROC(grid_search_cv, X_train, y_train, X_test, y_test, train=False)
results = pd.DataFrame(grid_search_cv.cv_results_)
printFullRow(results[results['rank_test_score'] == 1])
# best param setting: n_neighbors == 11/13, p ==2, weights = distance
return
def useAdaboost():
train = getTrainingData('train.csv', visualize=False)
X = train.drop(['Exited'], axis=1)
sc = StandardScaler()
X = sc.fit_transform(X)
y = train.Exited
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
adaboost(X_train, X_test, y_train, y_test)
def useGnnb():
train = getTrainingData('train.csv', visualize=False)
X = train.drop(['Exited'], axis=1)
y = train.Exited
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=0)
X_train, X_test = standard(X_train, X_test)
gnnb(X_train, X_test, y_train, y_test)