title: ValueError: X has 2 features, but SVC is expecting 32 features as input
body: <p>I encountered a problem as the title showed and didn't know how to fix it. I want to use cross-validation to analyze chronic disease. Could someone help me? thanks</p>
  <pre><code>from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 
  0.2, random_state = 0)
  from sklearn.preprocessing import StandardScaler
  sc_X = StandardScaler()
  X_train = sc_X.fit_transform(X_train)
  X_test = sc_X.transform(X_test)    
  from sklearn.svm import SVC
  classifier = SVC(kernel='rbf', random_state=0)
  classifier.fit(X_train,y_train)
  
  # Predicting the test set results
  y_pred = classifier.predict(X_test)
  
  # Making the Confusion Matrix
  from sklearn.metrics import confusion_matrix
  ckd = confusion_matrix(y_test, y_pred)
  ckd
  from matplotlib.colors import ListedColormap
  X_set, y_set = X_train, y_train
  X1, X2 =np.meshgrid(np.arange(start =X_set[:,0].min()-1, stop= X_set[:, 
  0].max()+1,step = 
  0.01), np.arange(start =X_set[:,1].min()-1, stop= 
  X_set[:,1].max()+1,step = 0.01))
  plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), 
  X2.ravel()]).T).reshape(X1.shape), #this line error
  alpha = 0.15, cmap = ListedColormap(('red', 'green')))
  plt.xlim(X1.min(),X1.max())
  plt.xlim(X1.min(),X1.max())
  for i, j in enumerate(np.unique(y_set)):
      plt.scatter(X_set[y_set == j, 0], X_set[y_set ==j, 1],
              c= ListedColormap(('red','green'))(i),label = j)
  plt.title('Kernel SVM (Training set)')
  plt.xlabel('Age')
  plt.ylabel('Estimated Salary')
  plt.legend()
  </code></pre>