使用MLPClassifier对葡萄酒数据集进行分类

首先，让我们加载葡萄酒数据集并将其拆分为训练和测试集：

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split

wine = load_wine()
X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3, random_state=42)

接下来，我们将导入 `MLPClassifier` 并用默认参数创建模型：

from sklearn.neural_network import MLPClassifier

mlp = MLPClassifier()

现在，我们可以使用 `fit()` 函数拟合模型：

mlp.fit(X_train, y_train)

最后，我们可以使用 `predict()` 函数预测测试集的标签：

y_pred = mlp.predict(X_test)

我们可以使用 `accuracy_score` 函数来计算模型在测试集上的准确率：

from sklearn.metrics import accuracy_score

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy: {:.2f}%".format(accuracy*100))

完整代码如下：

from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.metrics import accuracy_score

wine = load_wine()
X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3, random_state=42)

mlp = MLPClassifier()
mlp.fit(X_train, y_train)

y_pred = mlp.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)

print("Accuracy: {:.2f}%".format(accuracy*100))