ValueError：模型的特征数必须与输入匹配模型 n_features 为 30，输入 n_features 为 2

如何解决ValueError：模型的特征数必须与输入匹配模型 n_features 为 30，输入 n_features 为 2

我是数据科学和机器学习的新手。因此，我尝试使用从 here 中引用的隔离森林算法来可视化异常值。我正在使用 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,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]))

我从引用中修改了一点，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(-5,50))

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[:,X_test[:,c='green',edgecolor='k')
c = plt.scatter(X_outliers[:,X_outliers[:,c='red',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

我尝试将 (xx.shape) 更改为 X_train、X_test，但没有成功，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code