// iterate over datasets
for X, y in datasets:
// preprocess dataset, split into training and test part
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
After Change
alphas = np.logspace(-1, 1, 5)
classifiers = []
names = []
for alpha in alphas:
classifiers.append(make_pipeline(
StandardScaler(),
MLPClassifier(
