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我是数据科学和机器学习的新手。因此，我正在尝试使用我从此处引用的 Isolation Forest Algorithm 来可视化异常值。我正在使用来自 Kaggle 的信用卡欺诈数据集，X = 1-30 列，y = 列类

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.35)

# Define X dan y

columns = data1.columns.tolist()

# Filter columns  
columns = [c for c in columns if c not in ["Class"]]

# Saving label Class in target
target = "Class"

# Identify state, value of X, y, X_outliers
state = np.random.RandomState(42)
X = data1[columns]
y = data1[target]
X_outliers = state.uniform(low=0, high=1, size=(X.shape[0], X.shape[1]))

我从引用的地方做了一些修改，这里是完整的代码

rng = np.random.RandomState(42)

clf = IsolationForest(n_estimators=100, max_samples='auto', 
                      contamination=outlier_fraction, 
                      random_state=state, verbose=0)

clf.fit(X_train)
y_pred_train = clf.predict(X_train)
y_pred_test = clf.predict(X_test)
y_pred_outliers = clf.predict(X_outliers)

# plot the line, the samples, and the nearest vectors to the plane
xx, yy = np.meshgrid(np.linspace(-5, 5, 50), np.linspace(-5, 5, 50))

vrb = np.c_[xx.ravel(), yy.ravel()]
Z = clf.decision_function(vrb)
Z = Z.reshape(xx.shape)

plt.title("IsolationForest")
plt.contourf(xx, yy, Z, cmap=plt.cm.Blues_r)

b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white',
                 s=20, edgecolor='k')
b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='green',
                 s=20, edgecolor='k')
c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='red',
                s=20, edgecolor='k')
plt.axis('tight')
plt.xlim((-5, 5))
plt.ylim((-5, 5))
plt.legend([b1, b2, c],
           ["training observations",
            "new regular observations", "new abnormal observations"],
           loc="upper left")
plt.show()

我认为这是因为 vrb.shape (2500, 2) 中的 y 与 X.shape (28481, 30) 不同。但我不知道如何使它相同

我试图将 (xx.shape) 更改为 X_train、X_test，但没有用，我不断收到错误

ValueError: Number of features of the model must match the input. Model n_features is 30 and input n_features is 2.

这是我的完整代码

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