title: Probabilistic SVM, regression
body: <p>I've currently implemented a probabilistic (at least I think so) for binary classes. Now I want to extend this approach for regression, and I'm trying to use it for the Boston dataset. Unfortunately, it seems like my algorithm is stuck, the code I'm currently running is looking like this:</p>
  
  <pre><code>from sklearn import decomposition
  from sklearn import svm
  from sklearn.model_selection import GridSearchCV
  from sklearn.model_selection import train_test_split
  import warnings
  warnings.filterwarnings("ignore")
  from sklearn.model_selection import train_test_split
  from sklearn.datasets import load_boston
  
  boston = load_boston()
  
  X = boston.data
  y = boston.target
  inputs_train, inputs_test, targets_train, targets_test = train_test_split(X, y, test_size=0.33, random_state=42)
  
  def plotting():
      param_C = [0.01, 0.1]
      param_grid = {'C': param_C, 'kernel': ['poly', 'rbf'], 'gamma': [0.1, 0.01]}
      clf = GridSearchCV(svm.SVR(), cv = 5, param_grid= param_grid)
      clf.fit(inputs_train, targets_train)
      clf = SVR(C=clf.best_params_['C'], cache_size=200, class_weight=None, coef0=0.0,
                decision_function_shape='ovr', degree=5, gamma=clf.best_params_['gamma'],
                kernel=clf.best_params_['kernel'],
                max_iter=-1, probability=True, random_state=None, shrinking=True,
                tol=0.001, verbose=False)
      clf.fit(inputs_train, targets_train)
      a = clf.predict(inputs_test[0])
      print(a)
  
  
  plotting()
  </code></pre>
  
  <p>Can someone tell me, what is wrong in this approach, It's not the fact that I get some error message (I know, I've suppresed them above), but the code never stops running. Any suggestions is hugely appreciated.</p>