我正在使用2类0-1的乳腺癌数据集，并且训练和准确性非常好，但是我将类数更改为8类0-7，并且我的机智Ml算法越来越低，但是同时ANN 97％的准确性可能是我犯了一个错误，但我不知道在哪里
y_pred：
[5 0 3 0 3 6 1 0 2 1 7 6 7 3 0 3 6 3 7 0 7 1 5 2 5 0 3 6 5 5 7 2 0 6 6 6 3
6 5 0 0 6 6 5 3 0 5 1 6 4 0 7 6 0 5 5 5 0 0 5 7 1 6 6 7 6 0 1 7 5 6 0 6 0
3 3 6 7 7 1 0 7 0 5 5 0 6 0 0 6 1 6 5 0 0 7 0 1 6 1 0 6 0 7 0 6 0 5 0 6 3
6 7 0 6 6 0 0 0 5 7 4 6 6 2 3 5 6 0 7 7 0 5 6 0 0 0 6 1 5 0 7 4 6 0 7 3 6
5 6 6 0 2 0 1 0 7 0 1 7 0 7 7 6 6 6 7 6 6 0 6 5 1 1 7 6 6 7 0 7 0 1 6 0]
y_test：
[1 0 1 6 4 6 1 0 1 3 0 2 6 3 0 1 0 7 0 0 6 6 5 6 2 6 3 6 5 6 7 6 5 7 0 2 3
6 5 0 7 2 6 4 0 0 2 6 3 7 7 1 3 6 5 0 2 7 0 7 6 0 1 7 6 6 0 4 7 0 0 0 6 0
3 5 0 0 7 6 0 0 7 0 6 7 7 2 7 1 1 5 5 3 7 4 7 2 2 4 0 0 0 7 0 2 0 6 0 6 1
7 6 0 6 0 0 1 0 6 6 7 6 6 7 0 6 1 0 0 7 0 5 7 0 0 7 7 6 5 0 0 1 6 0 7 6 6
5 2 6 0 2 0 6 0 5 0 2 7 0 7 7 6 7 6 6 6 0 6 6 0 1 1 7 6 2 7 6 0 0 6 5 0]
我已将multilabel_confusion_matrix替换为confusion_matrix，但仍然得到相同的结果，准确度在40％到50％之间。
而我得到的结果是：cv_results.mean() *100
K-Nearest Neighbours: 39.62 %
Support Vector Machine: 48.09 %
Naive Bayes: 30.46 %
Decision Tree: 30.46 %
Randoom Forest: 52.32 %
Logistic Regression: 44.26 %
这是Ml部分：
# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = np.argmax(y_pred, axis=1)
cm = multilabel_confusion_matrix(y_test, y_pred)
models = []
models.append(('K-Nearest Neighbours', KNeighborsClassifier(n_neighbors = 5)))
models.append(('Support Vector Machine', SVC()))
models.append(('Naive Bayes', GaussianNB()))
models.append(('Decision Tree', DecisionTreeClassifier()))
models.append(('Randoom Forest', RandomForestClassifier(n_estimators=100)))
models.append(('Logistic Regression', LogisticRegression()))
results = []
names = []
for name, model in models:
kfold = model_selection.KFold(n_splits=10, random_state = 8)
cv_results = model_selection.cross_val_score(model, X_train, y_train, cv=kfold, scoring='accuracy')