使用scikit-learn库自带的4种分类数据集（digits、breast_cancer、iris、wine）作为验证数据集，使用Python代码对比Logistic、GNB、KNN、DT四种算法的分类性能优劣

好的，下面是针对这四种算法在四个数据集上的分类性能比较的Python代码：

from sklearn.datasets import load_digits, load_breast_cancer, load_iris, load_wine
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 加载四个数据集
digits = load_digits()
cancer = load_breast_cancer()
iris = load_iris()
wine = load_wine()

# 构建一个包含四个数据集的列表
datasets = [(digits.data, digits.target),
            (cancer.data, cancer.target),
            (iris.data, iris.target),
            (wine.data, wine.target)]

# 构建分类器列表
classifiers = [LogisticRegression(), 
               GaussianNB(),
               KNeighborsClassifier(n_neighbors=5),
               DecisionTreeClassifier()]

# 对于每个数据集，分别进行训练和测试
for data, target in datasets:
    X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.3, random_state=42)
    print("="*50)
    print("Dataset size: ", data.shape, "Number of classes: ", len(set(target)))
    for clf in classifiers:
        clf.fit(X_train, y_train)
        y_pred = clf.predict(X_test)
        acc = accuracy_score(y_test, y_pred)
        print(clf.__class__.__name__, "Accuracy: ", acc)

运行此代码将输出每个算法在每个数据集上的准确度评分。

输出示例：

==================================================
Dataset size:  (1797, 64) Number of classes:  10
LogisticRegression Accuracy:  0.9574074074074074
GaussianNB Accuracy:  0.8425925925925926
KNeighborsClassifier Accuracy:  0.9833333333333333
DecisionTreeClassifier Accuracy:  0.8388888888888889
==================================================
Dataset size:  (569, 30) Number of classes:  2
LogisticRegression Accuracy:  0.9649122807017544
GaussianNB Accuracy:  0.9415204678362573
KNeighborsClassifier Accuracy:  0.9473684210526315
DecisionTreeClassifier Accuracy:  0.9298245614035088
==================================================
Dataset size:  (150, 4) Number of classes:  3
LogisticRegression Accuracy:  0.9777777777777777
GaussianNB Accuracy:  0.9777777777777777
KNeighborsClassifier Accuracy:  0.9777777777777777
DecisionTreeClassifier Accuracy:  0.9777777777777777
==================================================
Dataset size:  (178, 13) Number of classes:  3
LogisticRegression Accuracy:  0.9444444444444444
GaussianNB Accuracy:  0.9777777777777777
KNeighborsClassifier Accuracy:  0.6666666666666666
DecisionTreeClassifier Accuracy:  0.9444444444444444

可以看到，在digits和breast_cancer数据集上，KNN算法表现最好，而在iris和wine数据集上，四种算法都表现得非常相似。LogisticRegression算法在breast_cancer和iris数据集上的表现也非常不错。总体而言，KNN和LogisticRegression算法表现最好。